Doodling for Academics

In recent weeks, I have been slowing down a little in an attempt to find some of that ever-elusive ‘work-life balance’. Amongst other fledgling self-care efforts, I have started exercising again (following an unintentional but extensive hiatus) and reading more. Now, I am adding colouring to my list of relaxing activities, and here’s why.

A few months ago, I was contacted by University of Chicago Press asking if I’d be willing to take a look at a new book, Doodling for Academics by Julie Schumacher. This sounded like a lot of fun and the book is awesome, so I was happy to provide a blurb:

The wonderfully weird illustrations in Doodling for Academics brilliantly capture the bizarre highs, and arcane lows, of academic life. Full of fun activities to pass the time at staff meetings, this book will be a quirky addition to any academic office.

In Julie’s own words:

The original idea for Doodling for Academics came from University of Chicago Press editor Christie Henry. When she proposed it to me, my first instinct was to dismiss it, but then I found myself laughing while day-dreaming through a few possible images. I had never collaborated on a writing project before, so the matching of concept, illustration, title and caption for the forty different panels was initially overwhelming. Illustrator Lauren Nassef and I exchanged hundreds of emails and drafts, and had several very long conference calls, but never met until after the book was finished.

As soon as copies became available, my department chair held a coloring party. We stuck our finished artwork on the staff fridge when we were done.

I loved the book so much I asked the publisher if I could post a few free pages for fellow academic doodlers to print out and colour in. Three free doodles are provided below, and you can click here to get your hands on the full book, which contains 40 of these wonderfully silly and snarky illustrations.)

 

Julie Schumacher is professor of English and director of the Creative Writing Program at the University of Minnesota. She is the author of the best-selling Dear Committee Members, winner of the Thurber Prize for American Humor. Lauren Nassef is a freelance illustrator and artist living in Chicago.

Full disclosure: I didn’t get paid for the blurb, but I did get a free copy of the book. I also didn’t get paid for this post, I just wanted to share the fun so I reached out to the publisher for the free pages. I did however set up an Amazon affiliate account (an idea I had only when finding a link to buy the book for this post), which I believe means that if you by the book after clicking the link above I will get 3 cents or something.

6 Phrases that Should be Banned

By Dr George Gosling

Academia, whether that means teaching or studying, is ultimately a matter of communication. Our words are the lifeblood of what we do. So I regularly find myself stuggling to suppress my inner pedant when I read phrases that I know simply don’t do what they’re supposed to. So, if for no other reason than to release the build up of pedantry, here are my top six offenders. Of course, these are things for which I’d never dream of marking down a student, but I might counsel them against. If you use them all the time, it’s nothing personal.

  • It could be argued that…

This is one that gets used endlessly in student essays, and it’s hard to blame them when it’s used so frequently in academic texts. Unfortunately it is absolutely meaningless. Anything could be argued. I could write a blog post putting forward an argument for the sun being The Great Mother Satsuma, but I’d struggle to make the case convincingly. One of the things students find hardest to master is acknowledging complexity while still putting forward a strong argument. For me, this is the wrong side of the line. Arguably, starting a sentence by sitting on the fence like this is a bad habit to get into, as you can easily find yourself opting for this over and over, and miss the fact you haven’t actually argued anything. If you’re not convinced, attribute it to someone who is.

  • On the other hand…

There is a simple way to structure an essay: argument, counter-argument, conclusion. It is easy, but I tend to advise against it. This is often a shock to those students who’ve had it drummed into them at A-Level. Structuring an essay this way is not wrong. It’s actually a straightforward way of producing an acceptable essay. However, it’s a really difficult way of writing really good essay. This is because it creates a number of traps – forcing you to simplify the discussion into two sides when it’s probably much more complex, and making it all too easy to avoid actually having an argument of your own until the closing sentences. No. Start with the argument and then make the case.

  • a biased source

In fact, in my seminars I recommend students ditch the term ‘bias’ altogether. There is no person, no document (no historical witness or source) that is not biased in some way or another. Again, it’s meaningless. The problem here is that labelling a source as biased sounds like you’ve actually said something when you haven’t, making it all too easy to move on to the next point without actually having made one at all. Instead, identify the perspective from which a source is written, or from which they see events. That really can tell us something.

  • some historians

What happened is history (the past). How we interpret, explain and debate the cause, impact and meaning of what happened is History (the scholarly discipline). This wouldn’t be possible if all historians agreed, so there is some sense in distinguishing between the ideas and opinions of some historians and others. The problem is the obvious question it prompts: which ones? Not specifying implies historians are interchangeable, that the positions we take are random. We’re not and they’re not. This is why labelling historians as traditionalist and revisionist likewise falls short – suggesting it’s a fluke of timing. Once again this phrase only does half the job.

  • …but then she is a feminist historian

The objective historian is a myth. Once we recognise we are all biased commentators it can serve as a useful myth – giving license to rigorously question our own assumptions against both the available evidence and the wisdom of the crowd. This is a good thing, yet it’s often cut short by the negative connotations of bias. Labelling the premise of the historian’s assumptions should be a helpful way of engaging with their perspective on the past, but instead is often used to dismiss alternative interpretations rashly. Most typically I see this dismissal – sometimes this bluntly – to reject the arguments of feminist historians. Although I’ve never encountered this said of a male historian.

  • as to

I used to use this all the time about a decade ago, and there’s no zealot like a convert. The reason as to why I turned against this unnecessary flourish is that it’s pretentious. I’ve never used it when speaking, so why when writing? It’s the over-compensating that comes from not feeling you have the authority to write about a given subject. There will always be an element of fake it ’til you make it, but this is too transparent a disguise it be any use. Good academic writing is a matter of saying complicated things as simply as possible. Decide what needs saying. Say it plainly. Then stop.

This post originally appeared on Dr George Gosling’s blog. It is reposted here under the terms of Creative Commons license BY-NC 4.0. Dr Gosling is a Historian of medicine and charity in modern Britain and beyond. Follow him on twitter @gcgosling.

On Commonplace Books

By Steven Hill

The commonplace book is a seventeenth century innovation, and the idea is a simple one: A notebook for capturing interesting quotes from reading, ideas, snippets of text for writings, diagrams, sketches, anything that comes to mind. Over time these notebooks developed into personal anthologies of thought and reflection, and were often accompanied by elaborate schemes of indexing, so that the entries could be located and themes extracted.

The age of the internet has the potential to be the golden age of the commonplace book. First we have an unprecedented opportunity to read and access texts of all sorts, and secondly it is simple – no more complicated than ‘copy and paste’ – to bring elements of text together into places where search tools allow the rapid compilation of themes.

A drawing from Henry Tiffin's Commonplace Book (1760)

A drawing from Henry Tiffin’s Commonplace Book (1760)
Source: Peabody Essex Museum

I have been using Evernote as a commonplace book for a number of years. All sorts of things get saved into my Evernote notebooks, some of them automatically, and then the search function allows later retrieval. For example, a quick search for ‘commonplace book’ reveals that, rather spookily I was contemplating drafting a blog post on the topic exactly a year ago today. I was also able to identify previous reading I had done about commonplace books, and a quote from ‘Where Good Ideas Come From: The Natural History of Innovation‘ by Steven Johnson:

The great minds of the period—Milton, Bacon, Locke—were zealous believers in the memory-enhancing powers of the commonplace book. In its most customary form, “commonplacing,” as it was called, involved transcribing interesting or inspirational passages from one’s reading, assembling a personalized encyclopedia of quotations.

The search also provided me with a link to a related piece I had read by James Gleick on digitising books.

The commonplace book was a powerful idea in the seventeenth century but digitised text takes it to a new level. This idea is explored and developed further by Johnson in a blog post. In this post Johnson points out that searching for text can, in an instant, assemble a type of commonplace book using an algorithm. The google search I linked to at the beginning of this post is an example. The search results are presented in a particular order, and to an extent that order is customised to the individual. A new association of words and ideas is being created, specific for the reader, and in no way predictable by the authors of the original texts:

What you see on [a Google search results] page is, in a very real sense, textual play: the recombining of words into new forms and associations that their original creators never dreamed of.

Johnson goes on to consider the value that is created through these new combinations of text:

When text is free to combine in new, surprising ways, new forms of value are created. Value for consumers searching for information, value for advertisers trying to share their messages with consumers searching for related topics, value for content creators who want an audience. And of course, value to the entity that serves as the middleman between all those different groups.

And there is a following discussion on paywalls and technologies that prevent text to be mined and combined in new ways. The whole, long post is well worth a read. His conclusion is that access to text and reasonable re-use rights are central to ensuring that the potential benefits of the internet-enabled commonplace book. In Johnson’s words we need text to be in a commonplace book, not a glass box.

This is one of the reason that open access to the scholarly literature is so important. At the moment much of the scholarly literature is, at best, in a glassbox and at worst in a locked chest for which only a select few hold the key. Not only does the scholarly literature need to be made more available, but also licensed in such a way that re-use and re-purposing is possible. As Cameron Neylon has recently argued permissive licensing is essential. Access through glass boxes, like the Access to Research initiative is also deeply limited in its value.

I wonder what those seventeenth century ‘commonplacers’ would make of all this. I think they would be amazed by the potential of the digital commonplace book, but shocked to see how we have locked away some of the most valuable text, preventing real value to be obtained.

This post originally appeared on Steven Hill’s blog, ‘Testing Hypotheses…‘ It is reposted here under the terms of Creative Commons license BY-NC-SA 3.0.  Steven is the Head of Research Policy at the Higher Education Funding Council for England. Follow Steven on twitter @stevenhill.

Sample Cover Letter for Journal Manuscript Resubmissions

By Roy F. Baumeister

Dear Sir, Madame, or Other:
Enclosed is our latest version of Ms # 85-02-22-RRRRR, that is, the re-re-re-revised revision of our paper. Choke on it. We have again rewritten the entire manuscript from start to finish. We even changed the goddamn running head! Hopefully we have suffered enough by now to satisfy even you and your bloodthirsty reviewers.

I shall skip the usual point-by-point description of every single change we made in response to the critiques. After all, it is fairly clear that your reviewers are less interested in details of scientific procedure than in working out their personality problems and sexual frustrations by seeking some kind of demented glee in the sadistic and arbitrary exercise of tyrannical power over helpless authors like ourselves who happen to fall into their clutches. We do understand that, in view of the misanthropic psychopaths you have on your editorial board, you need to keep sending them papers, for if they weren’t reviewing manuscripts they’d probably be out mugging old ladies or clubbing baby seals to death. Still, from this batch of reviewers, C was clearly the most hostile, and we request that you not ask him or her to review this revision. Indeed, we have mailed letter bombs to four or five people we suspected of being reviewer C, so if you send the manuscript back to them the review process could be unduly delayed.

Some of the reviewers’ comments we couldn’t do anything about. For example, if (as review C suggested) several of my recent ancestors were indeed drawn from other species, it is too late to change that. Other suggestions were implemented, however, and the paper has improved and benefited. Thus, you suggested that we shorten the manuscript by 5 pages, and we were able to accomplish this very effectively by altering the margins and printing the paper in a different font with a smaller typeface. We agree with you that the paper is much better this way.

One perplexing problem was dealing with suggestions #13-28 by Reviewer B. As you may recall (that is, if you even bother reading the reviews before doing your decision letter), that reviewer listed 16 works that he/she felt we should cite in this paper. These were on a variety of different topics, none of which had any relevance to our work that we could see. Indeed, one was an essay on the Spanish-American War from a high school literary magazine. The only common thread was that all 16 were by the same author, presumably someone whom Reviewer B greatly admires and feels should be more widely cited. To handle this, we have modified the Introduction and added, after the review of relevant literature, a subsection entitled “Review of Irrelevant Literature” that discusses these articles and also duly addresses some of the more asinine suggestions in the other reviews.

We hope that you will be pleased with this revision and will finally recognize how urgently deserving of publication this work is. If not, then you are an unscrupulous, depraved monster with no shred of human decency. You ought to be in a cage. May whatever heritage you come from be the butt of the next round of ethnic jokes. If you do accept it, however, we wish to thank you for your patience and wisdom throughout this process and to express our appreciation of your scholarly insights. To repay you, we would be happy to review some manuscripts for you; please send us the next manuscript that any of these reviewers submits to your journal.

Assuming you accept this paper, we would also like to add a footnote acknowledging your help with this manuscript and to point out that we liked the paper much better the way we originally wrote it but you held the editorial shotgun to our heads and forced us to chop, reshuffle, restate, hedge, expand, shorten, and in general convert a meaty paper into stir-fried vegetables. We couldn’t, or wouldn’t, have done it without your input.

Sincerely,

Still. Not. Significant.

This post originally appeared on Matthew Hankin’s blog, Probable Error. Follow Matthew on twitter @mc_hankins.

What to do if your p-value is just over the arbitrary threshold for ‘significance’ of p=0.05?

You don’t need to play the significance testing game – there are better methods, like quoting the effect size with a confidence interval – but if you do, the rules are simple: the result is either significant or it isn’t.

p_values

An explanation of p-values, by the excellent XKCD comics.

So if your p-value remains stubbornly higher than 0.05, you should call it ‘non-significant’ and write it up as such. The problem for many authors is that this just isn’t the answer they were looking for: publishing so-called ‘negative results’ is harder than ‘positive results’.

The solution is to apply the time-honoured tactic of circumlocution to disguise the non-significant result as something more interesting. The following list is culled from peer-reviewed journal articles in which (a) the authors set themselves the threshold of 0.05 for significance, (b) failed to achieve that threshold value for p and (c) described it in such a way as to make it seem more interesting.

As well as being statistically flawed (results are either significant or not and can’t be qualified), the wording is linguistically interesting, often describing an aspect of the result that just doesn’t exist. For example, “a trend towards significance” expresses non-significance as some sort of motion towards significance, which it isn’t: there is no ‘trend’, in any direction, and nowhere for the trend to be ‘towards’.

Some further analysis will follow, but for now here is the list in full (UPDATE: now in alpha-order):

  • (barely) not statistically significant (p=0.052)
  • a barely detectable statistically significant difference (p=0.073)
  • a borderline significant trend (p=0.09)
  • a certain trend toward significance (p=0.08)
  • a clear tendency to significance (p=0.052)
  • a clear trend (p<0.09)
  • a clear, strong trend (p=0.09)
  • a considerable trend toward significance (p=0.069)
  • a decreasing trend (p=0.09)
  • a definite trend (p=0.08)
  • a distinct trend toward significance (p=0.07)
  • a favorable trend (p=0.09)
  • a favourable statistical trend (p=0.09)
  • a little significant (p<0.1)
  • a margin at the edge of significance (p=0.0608)
  • a marginal trend (p=0.09)
  • a marginal trend toward significance (p=0.052)
  • a marked trend (p=0.07)
  • a mild trend (p<0.09)
  • a moderate trend toward significance (p=0.068)
  • a near-significant trend (p=0.07)
  • a negative trend (p=0.09)
  • a nonsignificant trend (p<0.1)
  • a nonsignificant trend toward significance (p=0.1)
  • a notable trend (p<0.1)
  • a numerical increasing trend (p=0.09)
  • a numerical trend (p=0.09)
  • a positive trend (p=0.09)
  • a possible trend (p=0.09)
  • a possible trend toward significance (p=0.052)
  • a pronounced trend (p=0.09)
  • a reliable trend (p=0.058)
  • a robust trend toward significance (p=0.0503)
  • a significant trend (p=0.09)
  • a slight slide towards significance (p<0.20)
  • a slight tendency toward significance(p<0.08)
  • a slight trend (p<0.09)
  • a slight trend toward significance (p=0.098)
  • a slightly increasing trend (p=0.09)
  • a small trend (p=0.09)
  • a statistical trend (p=0.09)
  • a statistical trend toward significance (p=0.09)
  • a strong tendency towards statistical significance (p=0.051)
  • a strong trend (p=0.077)
  • a strong trend toward significance (p=0.08)
  • a substantial trend toward significance (p=0.068)
  • a suggestive trend (p=0.06)
  • a trend close to significance (p=0.08)
  • a trend significance level (p=0.08)
  • a trend that approached significance (p<0.06)
  • a very slight trend toward significance (p=0.20)
  • a weak trend (p=0.09)
  • a weak trend toward significance (p=0.12)
  • a worrying trend (p=0.07)
  • all but significant (p=0.055)
  • almost achieved significance (p=0-065)
  • almost approached significance (p=0.065)
  • almost attained significance (p<0.06)
  • almost became significant (p=0.06)
  • almost but not quite significant (p=0.06)
  • almost clinically significant (p<0.10)
  • almost insignificant (p>0.065)
  • almost marginally significant (p>0.05)
  • almost non-significant (p=0.083)
  • almost reached statistical significance (p=0.06)
  • almost significant (p=0.06)
  • almost significant tendency (p=0.06)
  • almost statistically significant (p=0.06)
  • an adverse trend (p=0.10)
  • an apparent trend (p=0.286)
  • an associative trend (p=0.09)
  • an elevated trend (p<0.05)
  • an encouraging trend (p<0.1)
  • an established trend (p<0.10)
  • an evident trend (p=0.13)
  • an expected trend (p=0.08)
  • an important trend (p=0.066)
  • an increasing trend (p<0.09)
  • an interesting trend (p=0.1)
  • an inverse trend toward significance (p=0.06)
  • an observed trend (p=0.06)
  • an obvious trend (p=0.06)
  • an overall trend (p=0.2)
  • an unexpected trend (p=0.09)
  • an unexplained trend (p=0.09)
  • an unfavorable trend (p<0.10)
  • appeared to be marginally significant (p<0.10)
  • approached acceptable levels of statistical significance (p=0.054)
  • approached but did not quite achieve significance (p>0.05)
  • approached but fell short of significance (p=0.07)
  • approached conventional levels of significance (p<0.10)
  • approached near significance (p=0.06)
  • approached our criterion of significance (p>0.08)
  • approached significant (p=0.11)
  • approached the borderline of significance (p=0.07)
  • approached the level of significance (p=0.09)
  • approached trend levels of significance (p0.05)
  • approached, but did reach, significance (p=0.065)
  • approaches but fails to achieve a customary level of statistical significance (p=0.154)
  • approaches statistical significance (p>0.06)
  • approaching a level of significance (p=0.089)
  • approaching an acceptable significance level (p=0.056)
  • approaching borderline significance (p=0.08)
  • approaching borderline statistical significance (p=0.07)
  • approaching but not reaching significance (p=0.53)
  • approaching clinical significance (p=0.07)
  • approaching close to significance (p<0.1)
  • approaching conventional significance levels (p=0.06)
  • approaching conventional statistical significance (p=0.06)
  • approaching formal significance (p=0.1052)
  • approaching independent prognostic significance (p=0.08)
  • approaching marginal levels of significance p<0.107)
  • approaching marginal significance (p=0.064)
  • approaching more closely significance (p=0.06)
  • approaching our preset significance level (p=0.076)
  • approaching prognostic significance (p=0.052)
  • approaching significance (p=0.09)
  • approaching the traditional significance level (p=0.06)
  • approaching to statistical significance (p=0.075)
  • approaching, although not reaching, significance (p=0.08)
  • approaching, but not reaching, significance (p<0.09)
  • approximately significant (p=0.053)
  • approximating significance (p=0.09)
  • arguably significant (p=0.07)
  • as good as significant (p=0.0502)
  • at the brink of significance (p=0.06)
  • at the cusp of significance (p=0.06)
  • at the edge of significance (p=0.055)
  • at the limit of significance (p=0.054)
  • at the limits of significance (p=0.053)
  • at the margin of significance (p=0.056)
  • at the margin of statistical significance (p<0.07)
  • at the verge of significance (p=0.058)
  • at the very edge of significance (p=0.053)
  • barely below the level of significance (p=0.06)
  • barely escaped statistical significance (p=0.07)
  • barely escapes being statistically significant at the 5% risk level (0.1>p>0.05)
  • barely failed to attain statistical significance (p=0.067)
  • barely fails to attain statistical significance at conventional levels (p<0.10
  • barely insignificant (p=0.075)
  • barely missed statistical significance (p=0.051)
  • barely missed the commonly acceptable significance level (p<0.053)
  • barely outside the range of significance (p=0.06)
  • barely significant (p=0.07)
  • below (but verging on) the statistical significant level (p>0.05)
  • better trends of improvement (p=0.056)
  • bordered on a statistically significant value (p=0.06)
  • bordered on being significant (p>0.07)
  • bordered on being statistically significant (p=0.0502)
  • bordered on but was not less than the accepted level of significance (p>0.05)
  • bordered on significant (p=0.09)
  • borderline conventional significance (p=0.051)
  • borderline level of statistical significance (p=0.053)
  • borderline significant (p=0.09)
  • borderline significant trends (p=0.099)
  • close to a marginally significant level (p=0.06)
  • close to being significant (p=0.06)
  • close to being statistically significant (p=0.055)
  • close to borderline significance (p=0.072)
  • close to the boundary of significance (p=0.06)
  • close to the level of significance (p=0.07)
  • close to the limit of significance (p=0.17)
  • close to the margin of significance (p=0.055)
  • close to the margin of statistical significance (p=0.075)
  • closely approaches the brink of significance (p=0.07)
  • closely approaches the statistical significance (p=0.0669)
  • closely approximating significance (p>0.05)
  • closely not significant (p=0.06)
  • closely significant (p=0.058)
  • close-to-significant (p=0.09)
  • did not achieve conventional threshold levels of statistical significance (p=0.08)
  • did not exceed the conventional level of statistical significance (p<0.08)
  • did not quite achieve acceptable levels of statistical significance (p=0.054)
  • did not quite achieve significance (p=0.076)
  • did not quite achieve the conventional levels of significance (p=0.052)
  • did not quite achieve the threshold for statistical significance (p=0.08)
  • did not quite attain conventional levels of significance (p=0.07)
  • did not quite reach a statistically significant level (p=0.108)
  • did not quite reach conventional levels of statistical significance (p=0.079)
  • did not quite reach statistical significance (p=0.063)
  • did not reach the traditional level of significance (p=0.10)
  • did not reach the usually accepted level of clinical significance (p=0.07)
  • difference was apparent (p=0.07)
  • direction heading towards significance (p=0.10)
  • does not appear to be sufficiently significant (p>0.05)
  • does not narrowly reach statistical significance (p=0.06)
  • does not reach the conventional significance level (p=0.098)
  • effectively significant (p=0.051)
  • equivocal significance (p=0.06)
  • essentially significant (p=0.10)
  • extremely close to significance (p=0.07)
  • failed to reach significance on this occasion (p=0.09)
  • failed to reach statistical significance (p=0.06)
  • fairly close to significance (p=0.065)
  • fairly significant (p=0.09)
  • falls just short of standard levels of statistical significance (p=0.06)
  • fell (just) short of significance (p=0.08)
  • fell barely short of significance (p=0.08)
  • fell just short of significance (p=0.07)
  • fell just short of statistical significance (p=0.12)
  • fell just short of the traditional definition of statistical significance (p=0.051)
  • fell marginally short of significance (p=0.07)
  • fell narrowly short of significance (p=0.0623)
  • fell only marginally short of significance (p=0.0879)
  • fell only short of significance (p=0.06)
  • fell short of significance (p=0.07)
  • fell slightly short of significance (p>0.0167)
  • fell somewhat short of significance (p=0.138)
  • felt short of significance (p=0.07)
  • flirting with conventional levels of significance (p>0.1)
  • heading towards significance (p=0.086)
  • highly significant (p=0.09)
  • hint of significance (p>0.05)
  • hovered around significance (p = 0.061)
  • hovered at nearly a significant level (p=0.058)
  • hovering closer to statistical significance (p=0.076)
  • hovers on the brink of significance (p=0.055)
  • in the edge of significance (p=0.059)
  • in the verge of significance (p=0.06)
  • inconclusively significant (p=0.070)
  • indeterminate significance (p=0.08)
  • indicative significance (p=0.08)
  • is just outside the conventional levels of significance
  • just about significant (p=0.051)
  • just above the arbitrary level of significance (p=0.07)
  • just above the margin of significance (p=0.053)
  • just at the conventional level of significance (p=0.05001)
  • just barely below the level of significance (p=0.06)
  • just barely failed to reach significance (p<0.06)
  • just barely insignificant (p=0.11)
  • just barely statistically significant (p=0.054)
  • just beyond significance (p=0.06)
  • just borderline significant (p=0.058)
  • just escaped significance (p=0.07)
  • just failed significance (p=0.057)
  • just failed to be significant (p=0.072)
  • just failed to reach statistical significance (p=0.06)
  • just failing to reach statistical significance (p=0.06)
  • just fails to reach conventional levels of statistical significance (p=0.07)
  • just lacked significance (p=0.053)
  • just marginally significant (p=0.0562)
  • just missed being statistically significant (p=0.06)
  • just missing significance (p=0.07)
  • just on the verge of significance (p=0.06)
  • just outside accepted levels of significance (p=0.06)
  • just outside levels of significance (p<0.08)
  • just outside the bounds of significance (p=0.06)
  • just outside the conventional levels of significance (p=0.1076)
  • just outside the level of significance (p=0.0683)
  • just outside the limits of significance (p=0.06)
  • just outside the traditional bounds of significance (p=0.06)
  • just over the limits of statistical significance (p=0.06)
  • just short of significance (p=0.07)
  • just shy of significance (p=0.053)
  • just skirting the boundary of significance (p=0.052)
  • just tendentially significant (p=0.056)
  • just tottering on the brink of significance at the 0.05 level
  • just very slightly missed the significance level (p=0.086)
  • leaning towards significance (p=0.15)
  • leaning towards statistical significance (p=0.06)
  • likely to be significant (p=0.054)
  • loosely significant (p=0.10)
  • marginal significance (p=0.07)
  • marginally and negatively significant (p=0.08)
  • marginally insignificant (p=0.08)
  • marginally nonsignificant (p=0.096)
  • marginally outside the level of significance
  • marginally significant (p>=0.1)
  • marginally significant tendency (p=0.08)
  • marginally statistically significant (p=0.08)
  • may not be significant (p=0.06)
  • medium level of significance (p=0.051)
  • mildly significant (p=0.07)
  • missed narrowly statistical significance (p=0.054)
  • moderately significant (p>0.11)
  • modestly significant (p=0.09)
  • narrowly avoided significance (p=0.052)
  • narrowly eluded statistical significance (p=0.0789)
  • narrowly escaped significance (p=0.08)
  • narrowly evaded statistical significance (p>0.05)
  • narrowly failed significance (p=0.054)
  • narrowly missed achieving significance (p=0.055)
  • narrowly missed overall significance (p=0.06)
  • narrowly missed significance (p=0.051)
  • narrowly missed standard significance levels (p<0.07)
  • narrowly missed the significance level (p=0.07)
  • narrowly missing conventional significance (p=0.054)
  • near limit significance (p=0.073)
  • near miss of statistical significance (p>0.1)
  • near nominal significance (p=0.064)
  • near significance (p=0.07)
  • near to statistical significance (p=0.056)
  • near/possible significance(p=0.0661)
  • near-borderline significance (p=0.10)
  • near-certain significance (p=0.07)
  • nearing significance (p<0.051)
  • nearly acceptable level of significance (p=0.06)
  • nearly approaches statistical significance (p=0.079)
  • nearly borderline significance (p=0.052)
  • nearly negatively significant (p<0.1)
  • nearly positively significant (p=0.063)
  • nearly reached a significant level (p=0.07)
  • nearly reaching the level of significance (p<0.06)
  • nearly significant (p=0.06)
  • nearly significant tendency (p=0.06)
  • nearly, but not quite significant (p>0.06)
  • near-marginal significance (p=0.18)
  • near-significant (p=0.09)
  • near-to-significance (p=0.093)
  • near-trend significance (p=0.11)
  • nominally significant (p=0.08)
  • non-insignificant result (p=0.500)
  • non-significant in the statistical sense (p>0.05
  • not absolutely significant but very probably so (p>0.05)
  • not as significant (p=0.06)
  • not clearly significant (p=0.08)
  • not completely significant (p=0.07)
  • not completely statistically significant (p=0.0811)
  • not conventionally significant (p=0.089), but..
  • not currently significant (p=0.06)
  • not decisively significant (p=0.106)
  • not entirely significant (p=0.10)
  • not especially significant (p>0.05)
  • not exactly significant (p=0.052)
  • not extremely significant (p<0.06)
  • not formally significant (p=0.06)
  • not fully significant (p=0.085)
  • not globally significant (p=0.11)
  • not highly significant (p=0.089)
  • not insignificant (p=0.056)
  • not markedly significant (p=0.06)
  • not moderately significant (P>0.20)
  • not non-significant (p>0.1)
  • not numerically significant (p>0.05)
  • not obviously significant (p>0.3)
  • not overly significant (p>0.08)
  • not quite borderline significance (p>=0.089)
  • not quite reach the level of significance (p=0.07)
  • not quite significant (p=0.118)
  • not quite within the conventional bounds of statistical significance (p=0.12)
  • not reliably significant (p=0.091)
  • not remarkably significant (p=0.236)
  • not significant by common standards (p=0.099)
  • not significant by conventional standards (p=0.10)
  • not significant by traditional standards (p<0.1)
  • not significant in the formal statistical sense (p=0.08)
  • not significant in the narrow sense of the word (p=0.29)
  • not significant in the normally accepted statistical sense (p=0.064)
  • not significantly significant but..clinically meaningful (p=0.072)
  • not statistically quite significant (p<0.06)
  • not strictly significant (p=0.06)
  • not strictly speaking significant (p=0.057)
  • not technically significant (p=0.06)
  • not that significant (p=0.08)
  • not to an extent that was fully statistically significant (p=0.06)
  • not too distant from statistical significance at the 10% level
  • not too far from significant at the 10% level
  • not totally significant (p=0.09)
  • not unequivocally significant (p=0.055)
  • not very definitely significant (p=0.08)
  • not very definitely significant from the statistical point of view (p=0.08)
  • not very far from significance (p<0.092)
  • not very significant (p=0.1)
  • not very statistically significant (p=0.10)
  • not wholly significant (p>0.1)
  • not yet significant (p=0.09)
  • not strongly significant (p=0.08)
  • noticeably significant (p=0.055)
  • on the border of significance (p=0.063)
  • on the borderline of significance (p=0.0699)
  • on the borderlines of significance (p=0.08)
  • on the boundaries of significance (p=0.056)
  • on the boundary of significance (p=0.055)
  • on the brink of significance (p=0.052)
  • on the cusp of conventional statistical significance (p=0.054)
  • on the cusp of significance (p=0.058)
  • on the edge of significance (p>0.08)
  • on the limit to significant (p=0.06)
  • on the margin of significance (p=0.051)
  • on the threshold of significance (p=0.059)
  • on the verge of significance (p=0.053)
  • on the very borderline of significance (0.05<p<0.06)
  • on the very fringes of significance (p=0.099)
  • on the very limits of significance (0.1>p>0.05)
  • only a little short of significance (p>0.05)
  • only just failed to meet statistical significance (p=0.051)
  • only just insignificant (p>0.10)
  • only just missed significance at the 5% level
  • only marginally fails to be significant at the 95% level (p=0.06)
  • only marginally nearly insignificant (p=0.059)
  • only marginally significant (p=0.9)
  • only slightly less than significant (p=0.08)
  • only slightly missed the conventional threshold of significance (p=0.062)
  • only slightly missed the level of significance (p=0.058)
  • only slightly missed the significance level (p=0·0556)
  • only slightly non-significant (p=0.0738)
  • only slightly significant (p=0.08)
  • partial significance (p>0.09)
  • partially significant (p=0.08)
  • partly significant (p=0.08)
  • perceivable statistical significance (p=0.0501)
  • possible significance (p<0.098)
  • possibly marginally significant (p=0.116)
  • possibly significant (0.05<p>0.10)
  • possibly statistically significant (p=0.10)
  • potentially significant (p>0.1)
  • practically significant (p=0.06)
  • probably not experimentally significant (p=0.2)
  • probably not significant (p>0.25)
  • probably not statistically significant (p=0.14)
  • probably significant (p=0.06)
  • provisionally significant (p=0.073)
  • quasi-significant (p=0.09)
  • questionably significant (p=0.13)
  • quite close to significance at the 10% level (p=0.104)
  • quite significant (p=0.07)
  • rather marginal significance (p>0.10)
  • reached borderline significance (p=0.0509)
  • reached near significance (p=0.07)
  • reasonably significant (p=0.07)
  • remarkably close to significance (p=0.05009)
  • resides on the edge of significance (p=0.10)
  • roughly significant (p>0.1)
  • scarcely significant (0.05<p>0.1)
  • significant at the .07 level
  • significant tendency (p=0.09)
  • significant to some degree (0<p>1)
  • significant, or close to significant effects (p=0.08, p=0.05)
  • significantly better overall (p=0.051)
  • significantly significant (p=0.065)
  • similar but not nonsignificant trends (p>0.05)
  • slight evidence of significance (0.1>p>0.05)
  • slight non-significance (p=0.06)
  • slight significance (p=0.128)
  • slight tendency toward significance (p=0.086)
  • slightly above the level of significance (p=0.06)
  • slightly below the level of significance (p=0.068)
  • slightly exceeded significance level (p=0.06)
  • slightly failed to reach statistical significance (p=0.061)
  • slightly insignificant (p=0.07)
  • slightly less than needed for significance (p=0.08)
  • slightly marginally significant (p=0.06)
  • slightly missed being of statistical significance (p=0.08)
  • slightly missed statistical significance (p=0.059)
  • slightly missed the conventional level of significance (p=0.061)
  • slightly missed the level of statistical significance (p<0.10)
  • slightly missed the margin of significance (p=0.051)
  • slightly not significant (p=0.06)
  • slightly outside conventional statistical significance (p=0.051)
  • slightly outside the margins of significance (p=0.08)
  • slightly outside the range of significance (p=0.09)
  • slightly outside the significance level (p=0.077)
  • slightly outside the statistical significance level (p=0.053)
  • slightly significant (p=0.09)
  • somewhat marginally significant (p>0.055)
  • somewhat short of significance (p=0.07)
  • somewhat significant (p=0.23)
  • somewhat statistically significant (p=0.092)
  • strong trend toward significance (p=0.08)
  • sufficiently close to significance (p=0.07)
  • suggestive but not quite significant (p=0.061)
  • suggestive of a significant trend (p=0.08)
  • suggestive of statistical significance (p=0.06)
  • suggestively significant (p=0.064)
  • tailed to insignificance (p=0.1)
  • tantalisingly close to significance (p=0.104)
  • technically not significant (p=0.06)
  • teetering on the brink of significance (p=0.06)
  • tend to significant (p>0.1)
  • tended to approach significance (p=0.09)
  • tended to be significant (p=0.06)
  • tended toward significance (p=0.13)
  • tendency toward significance (p approaching 0.1)
  • tendency toward statistical significance (p=0.07)
  • tends to approach significance (p=0.12)
  • tentatively significant (p=0.107)
  • too far from significance (p=0.12)
  • trend bordering on statistical significance (p=0.066)
  • trend in a significant direction (p=0.09)
  • trend in the direction of significance (p=0.089)
  • trend significance level (p=0.06)
  • trend toward (p>0.07)
  • trending towards significance (p>0.15)
  • trending towards significant (p=0.099)
  • uncertain significance (p>0.07)
  • vaguely significant (p>0.2)
  • verged on being significant (p=0.11)
  • verging on significance (p=0.056)
  • verging on the statistically significant (p<0.1)
  • verging-on-significant (p=0.06)
  • very close to approaching significance (p=0.060)
  • very close to significant (p=0.11)
  • very close to the conventional level of significance (p=0.055)
  • very close to the cut-off for significance (p=0.07)
  • very close to the established statistical significance level of p=0.05 (p=0.065)
  • very close to the threshold of significance (p=0.07)
  • very closely approaches the conventional significance level (p=0.055)
  • very closely brushed the limit of statistical significance (p=0.051)
  • very narrowly missed significance (p<0.06)
  • very nearly significant (p=0.0656)
  • very slightly non-significant (p=0.10)
  • very slightly significant (p<0.1)
  • virtually significant (p=0.059)
  • weak significance (p>0.10)
  • weakened..significance (p=0.06)
  • weakly non-significant (p=0.07)
  • weakly significant (p=0.11)
  • weakly statistically significant (p=0.0557)
  • well-nigh significant (p=0.11)

What PhD Life is Really Like

Mairi Young is a PhD student at the University of Glasgow, researching why people are scared of the dentist (sort of). She is also a foodie and self-confessed junk food lover, blogging over at The Weegie Kitchen.

When you’re studying for a PhD, you will be perpetually presented with two semi-rhetorical questions:

  1. Wow, you must be really smart?
  2. Wow, so you’re gonna be a Doctor!?

Regardless of how tedious these become, you better get used to it because it’s all any non-PhD-student really understands about it. We minions in the lower echelons of academia know it’s a different story altogether.

Whether you’re embarking on a PhD, you know someone studying a PhD and you want to understand their life a little better, or if you’re doing a PhD and just procrastinating today (I’m not here to judge, man) I’ll share what PhD life is really like.

Source: exloringhandhygiene.wordpress.com

Source: exloringhandhygiene.wordpress.com

We’re old, and a student

PhD students are generally older than your average Undergrad or Masters student. We have (considerably) less money than anyone else our age, we shop at Lidl and Aldi, and a night out/celebration are limited to:

  • Student clubs at the weekend – We can have a night out and taxi home for less than £20 but this involves warm syrupy cranberry juice mixed with paint stripper vodka in a plastic cup surrounded by girls wearing shorts/heels/crop tops and boys who resemble our baby brothers; or
  • Fancier pubs during the week – There are fewer crowds, so you can actually chat to your pals, cocktails come in REAL glasses and are often half price during the week. The problem is you can only really go out with your PhD pals because everyone else has to be in the office for 9am.
Source: author's personal collection.

Source: author’s personal collection.

What’s your PhD about anyway?

Let me tell you right now, 90% of people who ask this question aren’t interested in what your PhD is about at all. The other 10% is made up of:

  • Your Supervisors – You take up a lot of their time so naturally they are interested but this interest is VERY low down in their list of priorities;
  • People at a conference who are researching something similar – These people are the tiny percentage of people who actually understand your research and who are genuinely interested. Believe me, this is rare.

So how do you deal with this question from the other 90% of people who don’t care and are asking out of politeness? Well, you reel off a small catchy sums-it-up-sentence people can relate to

For example:

I’m researching why people are scared of attending the dentist.

People love this, and it generates a discussion that most people can join in with. Is it what my research is about? No.

My research evaluates the efficacy of interventions by oral health support workers trying to engage hard to reach families, typically people with a fear of attending the dentist, regarding oral health behaviours. My working title is:

Optimising the role of the Dental Health Support Worker in Childsmile Practice: A qualitative case study approach. 

You see the distinction?

The Doctor thing

Most people who know me, and my journey to get here, get excited about the whole ‘becoming a Doctor’ thing. I appreciate their support but I can’t share the enthusiasm because the shiny appeal of being Dr Mairi Young is well and truly lost.

Let me take you on a journey:

  • 3-6months into a PhD you’re worried about being found out as a fraud to even consider being awarded the doctorate. You’re convinced the University has made a mistake and will call you any day now to kick you out.
  • 1st year you have no idea what to do, so you wing it.
  • 2nd year you worry whether you’ve got enough time to do all your research and writing.
  • 3rd year you panic because you don’t think you’ve done enough to even put together a thesis.
  • By 4th year you’re worrying about PostDocs, Viva’s and the sheer cost of binding the thesis.

By the time your graduation comes around, you’re in the gown and you’re being handed the piece of paper which allows you to call yourself Doctor, you’re already in a Post Doc post and that journey has started all over again.

Forgive us if we aren’t all that excited about being called Doctor. It can feel like something of a consolation prize.

Endless corrections

11704948_10153079810253736_7499609512061010656_nThe one thing I miss about undergrad life was handing in an essay and never seeing it again. You’d receive a mark and that was it: a pure and beautiful cycle of hard work and reward.

This doesn’t happen at PhD level. 

In a PhD you will spend weeks, if not months, tirelessly working on a chapter to make it perfect. You will submit to your supervisors and wait. And wait. And wait. Then you get corrections back.

That beautiful piece of work you worked yourself to the bone for returns covered in incomprehensible scribbles. Deciphering these scribbles will become a skill fit for your CV. Once you’ve deciphered and amended the chapter to perfection, submitted the chapter and waited, and waited and waited…. You get corrections back again.

Thus it continues until the day the thesis is bound and submitted. It’s thoroughly de-motivating and an exhaustive task.

Endnote (or Zotero/Mendeley etc.)

If I could give PhD students one piece of advice for the future, it would be this: learn how to use Endnote.

I’m 2 years and 10 months into a 4 year PhD and I still don’t really know how to work EndNote.

Supposedly it makes your life easier because you can ‘cite while you write’ and compile your references at the click of a button. Yet as I still don’t really know how to work Endnote chances are I (and a couple other PhD’ers I know) will be typing ours out manually.

Please know I’m not lazy, and I’ve not been avoiding the issue. I simply never fully appreciated the time I had on my hands in the first 6 months of my PhD. Back then, I had all the time in the world to spend learning the detailed intricacies of useful software. 3 years in, I don’t have this luxury anymore.

phd 3

Council Tax

Quite possibly the saving grace of the whole PhD malarkey: No Council Tax.

I did my Undergraduate degree, MSc and PhD back to back which means I’ve been studying for 9 years (with a year to go *weeps*). I have not paid council tax at all during this time. I truly believe Glasgow City Council has a ‘Mairi Young Is At It / Must Investigate’ file because after 10 years at 3 different universities, they must be thinking “Surely she’s scamming us?”

Even though I receive a tax free stipend (which FYI is an absolute joy to explain to the Inland Revenue) it is a measly amount, so the saving I make not paying Council Tax means I can afford to live on my own, a luxury I never want to give up.

Undergrads

Contrary to popular opinion, PhD students don’t dislike Undergrads, we’re just jealous of them. Undergrads have it easy and they don’t even know it.

At that age you don’t mind living in a tiny single bedroom and sharing a kitchen with 7 other people so long as it’s within walking distance to class and the student union. You don’t mind living off 9p noodles, cereal and toast. You can sleep during the day between classes, be told exactly what to study to pass the module, submit an essay and never see it again, and you have a whole summer each year in which to relax and enjoy yourself.

PhD students don’t have such luxuries.

We’re too accustomed to the finer things in life: expensive complicated cocktails, antipasti and fresh flowers every weekend. We also read journal papers in bed to catch up with reading, which throws a downer on any romantic relationship, and we stress out over how to afford a suitable outfit for a conference on a measly PhD stipend.

If you’re an undergrad and you see a PhD student tutting at you in the library for browsing Asos rather than working, please know we don’t hate you, we’re just green with envy that our lives are no longer like that. I’m sure you can empathise. By the time you end up doing a PhD, you’ll feel the same, I promise.

The Workaholic and Academia: in defense of #AcaDowntime

Gemma Aherne is a PhD candidate at Leeds Beckett University. She originally posted this piece on her blog following a debate about #AcaDowntime on twitter. It is reposted here with permission. You can follow Gemma on twitter @princessjack.

palm

In 2012 my husband was in Intensive Care, or ICU as we like to call it in the UK. I was told he would most likely die. It was traumatic. My husband spent 3 weeks in an induced coma. It was terrifying. When he woke up still worrying about work more than his near-death predicament, a very wise nurse told him “There ain’t no pockets in a shroud”.

I can tell you there aren’t any publications either. Or titles. Or accolades.

I’m sure we have all had these moments. Losing loved ones, facing severe ill health, caring for ill relatives or friends. And we all see what matters in life is our health and wellbeing, and that of those we care about.

Yet in academia, workaholism is rife. It is normalised and dare I say it revered. If you loved your work enough you would answer emails whilst on annual leave/ at a funeral/ on maternity leave/ sick in hospital. You love your work, so why not do it 7 days a week?

Academia offers flexibility in working hours, which is why it appeals to me. But sadly flexibility often is often translated as available at all hours and working constantly. I’m here as an early career researcher to say no. If this is what academia is, I don’t want it. I love my work, I am so grateful to get paid work that I feel passionate about. But I am entitled to a life outside of it. We all are.

Spending time with your loved ones is not a privilege. Never let people tell you it is. Taking time out to rest, or watch Netflix, or read fiction, or watch films with your kids, or play with your pets, this is not a luxury. It keeps you well. Visiting your elderly relative in hospital, not a privilege. Seeing your old neighbour in a care home rather than taking on yet more additional work, not a privilege.

Not rushing back to work after a painful hospital appointment or upsetting health session, not a privilege. It’s called looking after yourself. I spent my time in 1st year trying to work the day after 2 operations, during my husband being re-admitted to hospital and the day my mum told me she was ill. Utter nonsense! Why? Because I felt guilty for a second off. Guilt and fear that I wouldn’t catch back up.

I wrote about self-care and the Ph.D here and I wrote about the trauma of research here. I have written on Happy Ph.D and M.E here, and blogging with health problems here.

Today I am working on a rare Sunday. Why? Because I am visiting 2 babies tomorrow on Monday afternoon. Since Christmas I have limited my working hours. I am more productive as a result, healthier, happier, and all my relationships have improved. I am a workaholic by nature, it’s anxiety for me and having health issues that I have to pace and deal with. But at Christmas when I felt guilty for having time off, I said no more. Friends on minimum wage didn’t feel guilty for taking time off. They felt lucky if they were able to take some time off, but not guilty. Why is it that guilt is so common place in academia?

I now do 40 hours a week. In marking season I will do more, or if I get a wave of energy for writing a chapter I will binge. But I take days off. Weekends off. I make plans. I enjoy my life outside of academia. I took my first holiday abroad in years in June. It was glorious. And that’s ok. It doesn’t make one less committed.

Today I see the #AcaDowntime hashtag. How good I thought! Let’s challenge current working expectations. It is not privilege to have rest time. Yes, we have times in our life where we are juggling jobs working all the hours we can to survive, but let’s call that out. Let’s not compound it as legitimate. It’s not showing off to join in with the hashtag, it’s challenging the dominant narrative that we must live to work.

Recently I read this For Slow Scholarship: A Feminist Politics of Resistance through Collective Action in the Neoliberal University. Number 10 in the paper stood out to me:

Reach for the minimum (i.e. good enough is the new perfect). Rather than getting caught up in measuring worth by the number of peer-reviewed journal articles published or grant dollars procured, reach instead for the minimum numbers necessary to achieve important benchmarks (such as tenure and promotion). Reaching for the minimum allows for a focus on quality – rather than quantity – and acknowledges the need for balance. Imagine, too, an alternate CV or annual report with all of the other items of life included: relationships tended to, illnesses overcome, loved ones cared for, hobbies cultivated. Be unwilling to be undermined or belittled for not conforming to hegemonic agendas that are devoid of the responsibilities and joys of life beyond the ivory tower.

The authors continue:

Slowing down involves resisting neoliberal regimes of harried time by working with care while also caring for ourselves and others.

A feminist mode of slow scholarship works for deep reflexive thought, engaged research, joy in writing and working with concepts and ideas driven by our passions. As a feminist intervention, slow scholarship enables a feminist ethics of care that allows us to claim some time as our own, build shared time into everyday life, and help buffer each other from unrealistic and counterproductive norms that have become standard expectations. Slow scholarship has value in itself, in the quality of research and writing produced, and also enables us to create a humane and sustainable work environment and professional community that allows more of us to thrive within academia and beyond.

This all day long. Our colleagues and friends who are mothers shouldn’t be answering work related emails on maternity leave, or feeling their part-time position upon their return means they don’t care enough, or they are lacking. Our colleagues with health issues, or caring for family or friends with issues, should not feel they have to choose between some respite, however short, or doing the obligated extras.

Academia actively promotes workaholism and that’s wrong. We need to look after our health.

I love my research, I feel lucky that this is my job. And thus it is easy to get sucked into working non-stop. But I have other commitments and things that need tending to in my life. If I have to choose between extra work and my loved ones, or resting up, or enjoying a hobby outside of my job, I am going to pick the latter. Not because I am not committed enough but because that’s what keeps me well.

I love the fact that in the Psychology department of my university there are a wonderful bunch of critical feminist researchers. They don’t email outside of 8-6 Mon to Fri. They actively encourage life outside the academy. And they are successful, kind, caring, and bloody brilliant.

We need to work and we want to make a difference in the lives of marginalized groups. We are very lucky to have this opportunity. But let’s remember that to carve out time for ourselves, or to opt for a radically different format of working, is not selfish or lazy, it’s absolutely necessary.

I shall follow and support #AcaDowntime. And tomorrow I shall look forward to meeting those two babies.

7 Academic Struggles Predicted by Late 19th and Early 20th Century Autobiographies

avatar_cee947a27aed_64By Alice Violett. Alice is doing a PhD at the University of Essex on the public perceptions and personal experiences of only children in Britain between 1850 and 1950, and blogs at Alice in Academia. Alice likes reading, music, and cats.  You can follow Alice on Twitter @pokesqueak.

My PhD, which is partly about the experiences of only children between 1850 and 1950, has lead to me reading many, many autobiographies. Many are pretty dull (male politicians who skimmed over their childhood years, I’m looking at you!), while others were quite interesting, keeping me reading long after the useful information had been extracted.

Now and again I would come across a funny or odd anecdote that served no useful purpose for my thesis but seemed worth saving. So I did. Upon reflection, I realise I’ve essentially collected an allegory for the academic experience.

monk-big-book1. When the book you need to read is unnecessarily dense

Gay rights campaigner Antony Wright’s uncle, aunt and cousins moved to South Africa in 1933, prompting a wave of creative writing:

I wrote several stories about my cousins’ adventures in the African bush … One vivid phrase I remember (because it caused the grown-ups so much amusement) described how, hearing a loud noise, Bobbie turned and ‘beheld a lion clearing its throat’. Fiction has never been my strong point, and I suspect such purple prose might make even a Barbara Cartland blush. 1

2. When you’ll do anything to get a research assistant

As a child, artist W. Graham Robertson happened upon a spellbook with instructions on ‘how to raise a Fairy’:

“Take the blood of a white hen.” – How could I take the blood of any hen, let alone that the hen would be my uncle’s, and I felt sure he would not care for its blood to be taken? Would the Fairy object to the hen’s being cooked, as, if not, I could save some gravy from dinner?

I gave up on my study of Conjuration, which in most cases seemed to require a little private abattoir of one’s own, for want of proper professional guidance…2

3. When reviewer three questions your knowledge of your own work

Fuller-Maitland liked to play with the boys next door:

One day, as we were all playing in their garden, my nurse hung her head out of a side-window and shouted, “Master John, jest you come in, and don’t let them little Nickles teach you any more bad words!” … To me personally it was galling to be considered as the humble learner in the branch of study indicated. 3

4. When colleagues competitively compare workloads

Music critic John Fuller-Maitland’s aunts moved to Brighton for their health:

One was heard to say, “I hear that Anne calls herself the queen of invalids; and everybody in Brighton knows that I am the queen of invalids”. 4

Your colleague's conference is here. Probably one of these.

Your colleague’s conference is here. Probably one of these.

5. When your colleagues conferences are in more exotic climes than yours

Sociologist and historian Alan Fox, as an impartial outsider, was often called upon to judge which of his better-off friends had it best:

Which did I think was better; ten days on the Belgian coast or three weeks at Dovercourt?5 

[NB: Dovercourt was a popular English seaside resort in the 1920s and 1930s, but is much less glamorous these days!

What even is that?

What even is that?
Credit: a hilarious complaint letter to Virgin, published by the Telegraph.

6. When the conference food options are puzzling

Fuller-Maitland’s parents were worried about his health:

I had to consume a sponge-cake and a glass of port in the middle of the morning for no special reason that I can recall. I have no doubt that this habit sowed the seeds of gout, from which I suffered a good deal at an unusually early age.6

7. When you need to impress an important potential contact

giphyAs a young boy, politician Sir George Leveson Gower was instructed by his uncle how to present a bouquet to the Empress Eugenie at a garden party:

When the Empress came the next day, I got confused, made my bow to my uncle, and as I presented my back to her and was dressed in a short white petticoat, the effect was unconventional.7

Some of the autobiographies I’ve read hadn’t been borrowed from the library in years, and it seemed like a shame to send them back without noting down some of their less relevant content.

Resurrect an old book from the storeroom today!

  1. A E G Wright (Antony Grey), Personal Tapestry, (London, 2008), p. 13.
  2.  W. Graham Robertson, Time Was, (London, 1931), p. 20.
  3.  J. A. Fuller-Maitland, A Door-Keeper of Music, (London, 1929), pp. 16-17.
  4.  J. A. Fuller-Maitland, A Door-keeper of Music, (London, 1929), p. 8.
  5.  Alan Fox, A Very Late Development: An Autobiography, (Coventry, 1990), p. 20.
  6. J. A. Fuller-Maitland, A Door-Keeper of Music, (London, 1929), p. 17.
  7. Sir George Leveson Gower, Years of Content, 1858-1886, (London, 1940), p. 2.

Academia: Survival of the Bitterest?

t4_-1069229323Jan Klimas is a scientist, artist, thinker and writer who’s interested in communicating with the public and using art to blend boundaries between the two disciplines. Check out his blog, and follow him on twitter @janklimas.

 

In dance, I call it Survival of the Bitterest. The choreographers who stick around are often the ones most comfortable feeling bitter and resentful. My artistic mentors were brilliant artists. But I do not want to live the lives they led.

Andrew Simonet

choreography-Amy-Siewert

Academia: a bitter dance for survival (Photo: David DeSilva)

What do dance and science have in common? What makes a successful choreographer or scientist? In this post, I speculate about the bitterness of the academic dance for survival. The academic competition is cruel and uneven. The fittest may not survive, but the bitterest thrive.

Before we dive deep into the murky academic waters, let’s define our objectives. Is survival worth the fight? Is it really a fight, or just a game? Survival is “a natural process resulting in the evolution of organisms best adapted to the environment” – academics would give anything to be the most evolved and the best adapted.

We strive to get tenure. Other occupations call it a permanent job. Few make it and most have to fulfill harsh criteria to keep their tenure, bringing in a lot of research funding, or taking on a heavy teaching load.

monkeys

Source: Oui Stock Images

Papers are the currency of our world. The one who has the most is the richest. Because money follows money, the more articles an academic co-authors, the higher her chances of getting more money (i.e., more research grants). Like tokens of appreciation, authorships on papers are gifts that some scientists give to each other as a gesture of appreciation, friendship or a promise of a future token. Agencies give grants to people with most of these tokens. Journal editors publish their friends’ work.

In such a system, the most published may not be the best; instead they are the most popular or they know how to play the system. In such a system, novice scientists’ willingness to park their writing integrity is challenged. Some may find refuge in writing non-academic literature, but for most, the peer-reviewed “romance” pays the mortgage.

Some big research centres are like fiction factories. They pay people to write articles; the purpose of those articles is to bring in more cash. Fiction factories operate like famous brands, where the name of a famous academic becomes a brand instead of signifying who wrote the paper. James Patterson, for example, “heralded as the world’s best paid writer, is the world’s most successful fiction factory,” writes Michelle Demers. Just like Patterson, the chief scientist comes at the end of writing, puts a few finishing touches and their names on the final product.

The road to tenure is paved with the PhD students that an academic supervises. This inflates the need for scientifically-trained workforce whereas the sole purpose of taking on a PhD student is, in many cases, to get the professor closer to the tenure. We don’t need so many PhDs. “PhD ‘overproduction’ is not new and faculty retirements won’t solve it,” writes Melonie Fullick in her speculative diction at University Affairs, “Yet somehow no matter how many PhDs enroll and graduate, academic careers are the goal.”

Overproduction-dependent career progression and dubious writing practices are only two of the many symptoms of a sick system. The best way to navigate such an unhealthy organised science is to bring both passion and dispassion to the task. Build up a dispassionate, bulletproof shield of resilience, unless you are willing to get sick yourself.

Some are born resilient. But for most, it takes years to become hardy. Much like Erickson’s stages of psychosocial development, resilience grows in stages. A crisis happens at each stage of development. A developmental task must be fulfilled for progress into the next stage. The infant learns hope by resolving the trust vs. mistrust crisis. A young adult breaks through the isolation, discovers intimacy and acquires love.

My own anecdotal evidence suggests the following developmental stages of early academic career: i) solitude, ii) despair, iii) good science/bad science, iv) fear and loathing, and v) workaholism. Getting through these stages takes you pretty far on the bitter road, but get ready to be the bitterest if you want to stick around.

Stages of development

  • Solitude: For the extroverts, this stage is excruciating. As they focus on the work, their social networks suffer. The computer becomes their best friend. Introverts find working alone easier, but it can be hard at the start. The junior scientist embraces loneliness in exchange for better concentration.
  • Despair: Some come into academia with genuine prosocial intentions. When they hit the brick wall of loneliness and parked integrity, they collapse. Too much science is done only for the sake of science and for personal interest. Finding the right balance between the need for helping others and promoting oneself moves the young scientist to the next stage.
  • Good science vs. bad science: OK, so if I can’t change the world through science, let’s just do it right so that the bad guys don’t win. Unlike fairy tales, the good scientists don’t always win. Bad science informs policy. Bad science receives funding. The fight for good science is endless. New researchers must decide which side of the battle they join.
  • Fear and loathing: power and control, greed and envy are common in academia. Fear is a natural reaction of junior scientists towards the loathsome deeds of some senior scientists. Scientists are humans too. They err. Some err too much and don’t acknowledge their mistakes. It is up to the junior scientists then to stay or to leave the kitchen if they can’t stand the heat. Learning to detach resolves this developmental conflict.
  • Workaholism: The balance is not static. It changes all the time. Latch on to the dynamic, forget about the static. The early-career scholar’s task is to make a healthy lifestyle their number one work tool.

The path to academic success is rough and bitter. Bitterness is the key to survival, but happiness lies in enjoying the journey, rather than focusing on the bitter end.

Fun and Laughter in the Lab

coverThis week we have a guest post from Dr. Gail M. Seigel. I recently bought Gail’s book, ‘Academania: My Life in the Trenches of Biomedical Research’, which recounts some of her experiences from her 25+ year research career. You can buy it here. Gail’s book happens to have an entire chapter about having fun in the lab, so I asked her to do a guest post and start a conversation about having fun in the lab! Follow Gail on Twitter @eyedoc333.

I am thrilled to be an invited guest blogger this week for Academia Obscura. As a matter of professional introduction, I am a retinal cell biologist at SUNY Buffalo with 25+ years experience in biomedical research. I am a firm believer in working hard, but having as much fun as possible while doing so. With all of the bad news these days of funding cuts, low wages and poor job security, we all need to lighten up sometimes and have a good laugh.

buff

Buff the Gerbil

When Academia Obscura asked for photos of “Academics with Cats” on Twitter, I was the one who posted a photo of “Academics with Gerbils” just to be contrary. That’s how I roll.

Here is an excerpt from the book, from the chapter entitled “Lab Hijinks”:

Sometimes we scientists need a break from the serious work of the lab, especially during the challenges of graduate school. The long hours and delayed gratification of long-term experiments can inspire us to do silly things to break up the tedium and I am no exception.

It was April Fools’ Day and there were two large goldfish swimming in our lab’s 10-liter buffer dialysis tank. My thesis advisor had once joked that although the dialysis tank was empty at the time, one day there would be fish swimming in it. I made sure that his prediction would come true. Not to worry, though. Once the prank was over, I brought the goldfish home as pets and named them Src and Myc, two oncogenes that I was studying at the time. Src and Myc went on to live happy goldfish lives and the dialysis tank was used for experiments once again.

Many people wonder what it’s like to work in a lab on a day-to-day basis. Sometimes, it can be very serious. At other times, it can resemble a comedy sketch. Imagine bright yellow masking tape with the word “radioactive” in red lettering. It is normally used as a warning label for experiments that involve radiation. But if the tape is cut in half, the word “radio” becomes evident, ready to stick on the portable stereo system used for background music in the lab.

I think being able to laugh at ourselves can help get us through some of the darkest times. My happiest memories of graduate school are not of the exams, but of the lightheartedness and human-ness of the people around me. I may have forgotten fermentation pathways, but I’ll always remember the bacterial plate streaked in the pattern of a good-natured farewell: “GO AWAY, LARRY” and presented to a fellow student upon graduation. I’ve also forgotten the Krebs cycle but I still remember a lab’s proud display of plasticware that had been accidentally melted into contorted modern sculptures by the intense heat of the autoclave cycle.

The funniest things can happen without even a conscious effort on anyone’s part. I’m still amused by the thought of proof-reading a student’s thesis before the age of auto-correct and finding the phrase “picnic acid” instead of “picric acid”. Another time, while scheduling a meeting with a visiting scientist named Dr. Fu, I had to spell the scientist’s name on the phone. I became red-faced and apologetic as I told the caller, “F-U”. It doesn’t take much to find humor in the nooks and crannies of every day life, academic or otherwise.

snowmanI will leave you with a visual prank. This one is a snowman made from lab ice, a conical tube and aluminum foil. When a co-worker dumped ice into the lab sink and declared, “Someone should make a snowman out of this!” How could I not?

You must have your own stories of academic tomfoolery, pranks and silliness: we would love to read all about them! Tweet using the hashtag #LabLaughs and share your stories. And remember: Have fun, but be safe!

If you want more about lab hijinks, as well as stories of plagiarism, sabotage and academic mayhem, check out Gail’s book, ‘Academania: My Life in the Trenches of Biomedical Research‘.