Snappy answers to stupid questions: an evidence-based framework for responding to peer-review feedback
Bibliographic record
Abstract
BACKGROUND: Authors are inundated with feedback from peer reviewers. Although this feedback is usually helpful, it can also be incomprehensible, rude or plain silly. Inspired by Al Jaffe's classic comic from Mad Magazine, we sought to develop an evidenced-based framework for providing "snappy answers to stupid questions," in the hope of aiding emerging academics in responding appropriately to feedback from peer review. METHODS: We solicited, categorized and analyzed examples of silly feedback from peer reviewers using the grounded theory qualitative research paradigm from 50 key informants. The informants represented 15 different professions, 33 institutions and 11 countries (i.e., Australia, Barbados, Canada, Germany, Japan, New Zealand, South Africa, Sweden, Switzerland, UK and USA). RESULTS: We developed a Scale of Silliness (SOS) and a Scale of Belligerence (SOB) to facilitate the assessment of inadequate peer-review feedback and guide users in preparing suitable responses to it. The SOB score is tempered by users' current mood, as captured by the Mood Reflective Index (MRI), and dictates the Appropriate Degree of Response (ADR) for the particular situation. CONCLUSION: Designed using the highest quality of (most easily accessible anecdotal) evidence available, this framework may fill a significant gap in the research literature by helping emerging academics respond to silly feedback from peer reviewers. Although use of the framework to its full extent may have negative consequences (e.g., loss of promotion), its therapeutic value cannot be understated.
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How this classification was reachedexpand
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 | 0.697 | 0.642 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.043 | 0.016 |
| Science and technology studies | 0.021 | 0.067 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.019 | 0.038 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".