Bibliographic record
Abstract
Following my letter to QSS (Siler, 2021), Yves Gingras (2022) responded with a variety of bad faith arguments, ad hominem attacks, and hyperbole.Gingras repeatedly distorted what I actually wrote, then attacked the distortion.Straw men might be convenient interlocutors, and can provide ballast for hot takes, but seldom yield intellectual progress.In his letter, Gingras broadly posited a false dichotomy, with "rational," apolitical stalwarts (including himself ) protecting the integrity of modern science against an incursion of hysterical, moralizing social justice warriors hostile to unpopular truths.Not only does this perspective betray a facile understanding of modern scientific communication, it also entails the fallacious notion that scientific empirics and underlying values are mutually exclusive.Gingras made a baseless accusation that my letter exhibited "ignorance of the nature of publishing," since I criticized reviewers of Strumia (2021) for refusing to anonymously share their peer review reports.However, Gingras omitted the important detail from Waltman's (2021: fn1) editorial that reviewers of Strumia (2021) were asked to anonymously publish the reports.While the reviewers were within their rights to refuse, I stand by my opinion that this was a cowardly and unproductive response, especially since open science is a foundational value of QSS, and an open peer review program has since been implemented at the journal.Incredibly, Gingras doubled down with farfetched, scattershot accusations of "character assassination."Despite Gingras' hyperbolic accusations, nobody's character was put in peril by my letter.Next, Gingras added a 900-word tangent on the self-retraction of a 2020 Nature Communications article on gender and mentorship, which is largely irrelevant to my letter.Notably, I took no normative position on that article or retraction, beyond suggesting that open peer review can be beneficial with controversial articles.However, I will suggest to Gingras that when authors self-retract an article-forestalling further empirical scrutiny-public proclamations that empirics are nevertheless correct should be taken with a grain of salt.Gingras used further misinterpretations of my letter to fuel attacks that I made "bizarre" comparisons between Strumia (2021) and other controversial social science articles.I invoked analogous cases of controversial articles to provide historical evidence that editorial philosophies that promote "diverse" and "controversial" viewpoints for their own sake can entail dangerous slippery slopes.The bothsidesism espoused in Waltman's editorial can potentially be used as justification to publish anything.Gingras went as far to argue that we should congratulate QSS for providing a platform to debate Strumia (2021).By this logic, should we congratulate the former Lancet editors for creating space to "rationally debate" links between vaccines and autism, after they published the infamous Wakefield article in 1998?If someone is willing and able to meaningfully defend Strumia (2021) from the widespread theoretical and empirical
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.028 | 0.057 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".