Much ink has been spilled in the last week or so over the so-called “tone” problem in psychology, and what to do about it. I speak here, of course, of the now infamous (and as-yet unpublished) APS Observer column
by APS Past President Susan Fiske, in which she argues rather strenuously that psychology is in danger of falling prey to “mob rule” due to the proliferation of online criticism generated by “self-appointed destructo-critics” who “ignore ethical rules of conduct.”
Plenty of people have already weighed in on the topic (my favorite summary is Andrew Gelman’s take
), and to be honest, I don’t really have (m)any new thoughts to offer. But since that’s never stopped me before, I will now proceed to throw those thoughts at you anyway, just for good measure.
Since I’m verbose but not inconsiderate, I’ll summarize my main points way up here, so you don’t have to read 6,500 more words just to decide that you disagree with me. Basically, I argue the following points:
- There is nothing wrong with the general tone of our discourse in psychology at the moment.
- Even if there was something wrong with the tone of our discourse, it would be deeply counterproductive to waste our time talking about it in vague general terms.
- Fear of having one’s scientific findings torn apart by others is not unusual or pathological; it’s actually a completely normal–and healthy–feeling for a scientist.
- Appeals to fairness are not worth taking seriously unless the argument is pitched at the level of the entire scientific community, rather than just the sub-community one happens to belong to.
- When other scientists do things we don’t like, it’s pointless and counterproductive to question their motives. Continue reading
“Dearly Beloved,” The Graduate Student began. “We are gathered here to–”
“Again?” Samantha interrupted. “Again with the Dearly Beloved speech? Can’t we just start a meeting like a normal journal club for once? We’re discussing papers here, not holding a funeral.”
discuss papers,” said The Graduate Student indignantly. “In good time. But first, we have to follow the rules of Great Minds Journal Club. There’s a protocol, you know.”
Samantha was about to point out that she didn’t
know, because The Graduate Student was the sole author of the alleged rules, and the alleged rules had a habit of changing every week. But she was interrupted by the sound of the double doors at the back of the room swinging violently inwards. Continue reading
[The report below was collectively authored by participants at the Open Source, Open Science meeting, and has been cross-posted in other places.]
On March 19th and 20th, the Center for Open Science
hosted a small meeting in Charlottesville, VA, convened by COS and co-organized by Kaitlin Thaney (Mozilla Science Lab
) and Titus Brown
(UC Davis). People working across the open science ecosystem attended, including publishers, infrastructure non-profits, public policy experts, community builders, and academics.
Open Science has emerged into the mainstream, primarily due to concerted efforts from various individuals, institutions, and initiatives. This small, focused gathering brought together several of those community leaders. The purpose of the meeting was to define common goals, discuss common challenges, and coordinate on common efforts.
We had good discussions about several issues at the intersection of technology and social hacking including badging, improving standards for scientific APIs, and developing shared infrastructure. We also talked about coordination challenges due to the rapid growth of the open science community. At least three collaborative projects emerged from the meeting as concrete outcomes to combat the coordination challenges.
A repeated theme was how to make the value proposition of open science more explicit. Why should scientists become more open, and why should institutions and funders support open science? We agreed that incentives in science are misaligned with practices, and we identified particular pain points and opportunities to nudge incentives. Continue reading
Digital object identifiers (DOIs) are much sought-after commodities in the world of academic publishing. If you’ve never seen one, a DOI is a unique string associated with a particular digital object (most commonly a publication of some kind) that lets the internet know where to find the stuff you’ve written. For example, say you want to know where you can get a hold of an article titled, oh, say, Designing next-generation platforms for evaluating scientific output: what scientists can learn from the social web
. In the real world, you’d probably go to Google, type that title in, and within three or four clicks, you’d arrive at the document you’re looking for
. As it turns out, the world of formal resource location is fairly similar to the real world, except that instead of using Google, you go to a website called dx.DOI.org, and then you plug in the string ’10.3389/fncom.2012.00072′, which is the DOI associated with the aforementioned article. And then, poof, you’re automagically linked
directly to the original document, upon which you can gaze in great awe for as long as you feel comfortable.
Historically, DOIs have almost exclusively been issued by official-type publishers: Elsevier, Wiley, PLoS and such. Consequently, DOIs have had a reputation as a minor badge of distinction–probably because you’d traditionally only get one if your work was perceived to be important enough for publication in a journal that was (at least nominally) peer-reviewed. And perhaps because of this tendency to view the presence of a DOIs as something like an implicit seal of approval from the Great Sky Guild of Academic Publishing, many journals impose official or unofficial commandments to the effect that, when writing a paper, one shalt only citeth that which hath been DOI-ified. Continue reading
[This is the first of a two-part series motivating and introducing precis, a Python package for automated abbreviation of psychometric measures. In part I, I motivate the search for shorter measures by arguing that internal consistency is highly overrated. In part II, I describe some software that makes it relatively easy to act on this newly-acquired disregard by gleefully sacrificing internal consistency at the altar of automated abbreviation. If you’re interested in this general topic but would prefer a slightly
less ridiculous more academic treatment, read this paper with Hedwig Eisenbarth and Scott Lilienfeld, or take a look at look at the demo IPython notebook.
Developing a new questionnaire measure is a tricky business. There are multiple objectives one needs to satisfy simultaneously. Two important ones are:
- The measure should be reliable. Validity is bounded by reliability; a highly unreliable measure cannot support valid inferences, and is largely useless as a research instrument.
- The measure should be as short as is practically possible. Time is money, and nobody wants to sit around filling out a 300-item measure if a 60-item version will do.
Unfortunately, these two objectives are in tension with one another to some degree. Continue reading
A long, long time ago (in social media terms), I wrote a post defending Facebook against accusations of ethical misconduct related to a newly-published study in PNAS. I won’t rehash the study, or the accusations, or my comments in any detail here; for that, you can read the original post (I also recommend reading this or this for added context). While I stand by most of what I wrote, as is the nature of things, sometimes new information comes to light, and sometimes people say things that make me change my mind. So I thought I’d post my updated thoughts and reactions. I also left some additional thoughts in a comment on my last post, which I won’t rehash here.
Anyway, in no particular order…
I’m not arguing for a lawless world where companies can do as they like with your data
Some people apparently interpreted my last post as a defense of Facebook’s data use policy in general. It wasn’t. I probably brought this on myself in part by titling the post “In Defense of Facebook”. Maybe I should have called it something like “In Defense of this one particular study done by one Facebook employee”. In any case, I’ll reiterate: I’m categorically not saying that Facebook–or any other company, for that matter–should be allowed to do whatever it likes with its users’ data. There are plenty of valid concerns one could raise about the way companies like Facebook store, manage, and use their users’ data. And for what it’s worth, I’m generally in favor of passing new rules regulating the use of personal data in the private sector. So, contrary to what some posts suggested, I was categorically not advocating for a laissez-faire world in which large corporations get to do as they please with your information, and there’s nothing us little people can do about it. Continue reading
[UPDATE July 1st: I've now posted some additional thoughts in a second post here.]
It feels a bit strange to write this post’s title, because I don’t find myself defending Facebook very often. But there seems to be some discontent in the socialmediaverse at the moment over a new study in which Facebook data scientists conducted a large-scale–over half a million participants!–experimental manipulation on Facebook in order to show that emotional contagion occurs on social networks. The news that Facebook has been actively manipulating its users’ emotions has, apparently, enraged a lot of people.
Before getting into the sources of that rage–and why I think it’s misplaced–though, it’s worth describing the study and its results. Here’s a description of the basic procedure, from the paper:
The experiment manipulated the extent to which people (N = 689,003) were exposed to emotional expressions in their News Feed. This tested whether exposure to emotions led people to change their own posting behaviors, in particular whether exposure to emotional content led people to post content that was consistent with the exposure—thereby testing whether exposure to verbal affective expressions leads to similar verbal expressions, a form of emotional contagion. People who viewed Facebook in English were qualified for selection into the experiment. Two parallel experiments were conducted for positive and negative emotion: One in which exposure to friends’ positive emotional content in their News Feed was reduced, and one in which exposure to negative emotional content in their News Feed was reduced. In these conditions, when a person loaded their News Feed, posts that contained emotional content of the relevant emotional valence, each emotional post had between a 10% and 90% chance (based on their User ID) of being omitted from their News Feed for that specific viewing.