3. "Why the new Recognition & Rewards actually boosts excellent science."(🇳🇱&🇬🇧)
By a coalition of Dutch #openscience communities. You can sign the document at the bottom.
Dutch universities are making a move to abandon the impact factor in recognition and reward considerations. A group of 170 Dutch academics posted a critical response to this initiative. I summarize why these responses fail to convince me. 🧵
First, for context, here the initiative by @UniUtrecht we are talking about: changing rewards and recognitions. Other universities have similar initiatives.
Here the rebuttal by 171 academics in the Netherlands, most of whom appear to be full professors. It's in NL, but google translate works well for Dutch websites.
1/ National Institute for Health & Care Excellence does not recommend #esketamine to treat #depression bc effectiveness unclear (low quality trials), problematic economic model (short-term treatment, depression lasts long). Cost/benefit not sufficient to recommend treatment.
3/ Agreed that published literature is low quality. Samples are generally too small to draw inferences from the samples to the population; there are recent studies without placebo groups (how does that even get funded in 2020); when placebo groups exist, they are often not >>
"Hans-Ulrich Wittchen .. is under fire after an investigation into one of his studies found evidence of manipulation—and elaborate efforts to cover up the misdeed. The investigation report .. also shows Wittchen intimidated whistleblowers"
1/ A test helps to determine whether you have a feature or not.
Good tests are precise: they predict a feature well, have high sensitivity/specificity, & low false positives/negatives.
2/ Precise biological tests do not exist for the most common mental disorders. There are some weak biological correlates for depression, but a weak correlate is not a test, the same way that a weak correlate of COVID (coughing) is not a test for COVID.
Happy to share our new preprint with Edwin de Beurs, in which we recommend to solve the current dilemma "So-Many-Scales-For-The-Same-Construct" (e.g. for depression) by mandating a common metric, not by mandating a common measure.🧵
We introduce the problem of scale proliferation, and how it impacts not only science, but also communication (between researchers & policy makers; between clinicians; between clinicians & clients; etc).
Beavers, like all species, are convenient fiction. Thinking of beavers as true category in nature is pre-Darwinian.
Beavers, instead, are a number of animals that cluster together quite closely in an n-dimensional space on a large number of features.
Outrageous? Bear with me.🧵
What are these features? They include things like length, hairiness, intelligence, number of limbs, distance of ears to claws, and so on.
These features cluster together because they are causally related (often in complex ways).
Imagine this 2-feature plot, except with 3.7 billion features.
You can see that many elephants and many beavers clusters on 2 features. You can also see an outlier elephant (lots of hair) and an outlier beaver (exceptionally heavy).