Re-evaluation of solutions to the problem of unprofessionalism in peer review
Bibliographic record
Abstract
Our recent paper ( https://doi.org/10.1186/s41073-020-00096-x ) reported that 43% of reviewer comment sets (n=1491) shared with authors contained at least one unprofessional comment or an incomplete, inaccurate of unsubstantiated critique (IIUC). Publication of this work sparked an online (i.e., Twitter, Instagram, Facebook, and Reddit) conversation surrounding professionalism in peer review. We collected and analyzed these social media comments as they offered real-time responses to our work and provided insight into the views held by commenters and potential peer-reviewers that would be difficult to quantify using existing empirical tools (96 comments from July 24th to September 3rd, 2020). Overall, 75% of comments were positive, of which 59% were supportive and 16% shared similar personal experiences. However, a subset of negative comments emerged (22% of comments were negative and 6% were an unsubstantiated critique of the methodology), that provided potential insight into the reasons underlying unprofessional comments were made during the peer-review process. These comments were classified into three main themes: (1) forced niceness will adversely impact the peer-review process and allow for publication of poor-quality science (5% of online comments); (2) dismissing comments as not offensive to another person because they were not deemed personally offensive to the reader (6%); and (3) authors brought unprofessional comments upon themselves as they submitted substandard work (5%). Here, we argue against these themes as justifications for directing unprofessional comments towards authors during the peer review process. We argue that it is possible to be both critical and professional, and that no author deserves to be the recipient of demeaning ad hominem attacks regardless of supposed provocation. Suggesting otherwise only serves to propagate a toxic culture within peer review. While we previously postulated that establishing a peer-reviewer code of conduct could help improve the peer-review system, we now posit that priority should be given to repairing the negative cultural zeitgeist that exists in peer-review.
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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 | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchResearch integrityScholarly communication Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.506 | 0.770 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".