Monarch butterflies, and weird failures of observation

Photos: Wildlife-Friendly Garden and signage, © S Heard CC BY 4.0.  Monarch caterpillars on milkweed (in Minnesota), Courtney Celley/USFWS, CC BY 2.0.

My university, like many, is concerned with appearing green, and among its projects is a series of small plantings that offer (mostly) native plants with educational signage.  I pass by one of these every day on my walk to work: the “Wildlife-Friendly Garden”.  It has Joe Pye weed, roses, goldenrod, and a few other things, and it has some signs introducing passers-by to its “frequent visitors”.

One of the “frequent visitors”, we’re told, is the monarch butterfly: it has a lovely and informative sign.  This seems unremarkable: everyone loves monarch butterflies, everyone knows they’re common visitors to late-summer flowers like goldenrod and Joe Pye, and everyone knows they’re a species at risk* worth cherishing.  So how could I possibly have a beef with this sign? 

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Why journals like “reject, but resubmit”

It happened to me again, a few weeks ago: a manuscript I’d had high hopes for came back from the journal with a decision of “reject, but with an invitation to resubmit”.  It’s better than a flat-out reject, to be sure, but disappointing nonetheless.

There’s a widespread belief – almost a conspiracy theory – that journals use “reject, but resubmit” as a device to cheat on their handling time statistics (by which we mostly mean time from submission to first acceptance).  After all, if a manuscript gets “revision”, the clock keeps ticking from the original submission; but “reject, but resubmit” means we can pretend the resubmission is a brand new manuscript and start the clock over.  Clever but deceptive move, right? 

Originality is over-rated. (Including by me.)

Image: Tulips, Vera Kratochvil CC-0 released to public domain.

Last week I reviewed a grant proposal for one of the European national granting agencies.  It was an interesting piece of work, which – if funded – would gather probably our best dataset so far to test some longstanding questions in my field.  It was ambitious, thorough, and well planned.  But it didn’t blaze any particularly new path: the techniques were standard, the questions have been in the literature for decades, and every planned analysis has been done before (albeit with smaller and less suitable datasets).

Before I’d even quite noticed, I found that I’d written a sentence in my review saying “There’s nothing original about the proposed research”.  But as I looked at that sentence – and as it glared back at me from the screen – I felt like it was judging me more than the applicant.  And it should have.

You see, originality in science is highly over-rated. 

My weirdest job interview – and assumptions from familiarity

Image: Still from “Silly Job Interview”, Monty Pythons’ Flying Circus, Season 1 Episode 5.

Years ago, I went on a really, really weird job interview.  I’ve told the story many times since, but I’ve realized that the way I tell it has shifted.  There’s a moral there, only part of which is that I was dumb.  (Readers of Scientist Sees Squirrel really like stories about how I was dumb, and fortunately, it’s a deep well.)

As a postdoc, back in the mid-1990s, I applied for a lot of jobs (all of them in the university professoriate, at universities with at least some research emphasis).  Most of them, of course, I didn’t get. 

Statistics in Excel, and when a Results section is “too short”

Every now and again, you see a critique of a manuscript that brings you up short and makes you go “Huh”.

A student of mine defended her thesis a while ago, and one of her examiners commented on one of her chapters that “the Results section is too short”*.  Huh, I said.  Huh.

I’m quite used to seeing manuscripts that are too long.   Occasionally, I see a manuscript that’s too short.  But this complaint was more specific: that the Results section in particular was too short. I’d never heard that one, and I just couldn’t make sense of it.  Or at least, not until I realized that it fits in with another phenomenon that I see and hear a lot: the suggestion that nobody should ever, ever do their statistics in Excel.