Everybody hurts: The Reviewer‐Imposed Pain (RIP) matrix
Bibliographic record
Abstract
The peer review process supports authors by providing feedback on manuscripts from external, expert readers. However, not all reviews are supportive. Some reviews are just painful. But just how painful are they? In this study, we set out to validate a matrix describing the levels of pain authors experience in response to the sting of peer reviewer comments. The study was carried out in two phases. In Phase 1, we developed a matrix combining two scales-i.e. the Suffering Scale and the Grind Gauge. The first categorises review-induced pain across four levels, with Level 1 being the least pain and Level 4 the greatest pain. The second categorises the amount of work required by the author to respond to and address reviewer comments, with Level 1 being the least amount and Level 4 the greatest amount. In Phase 2, we tested the performance of the matrix by recruiting multiple, global study sites to provide performance data. This work resulted in the development and validation of the Reviewer-Imposed Pain (RIP) matrix. A statistician analysed our data and assures us that the RIP matrix is now a validated tool. Our study shows that the pain associated with academic peer review affects physiologic, affective, and cognitive dimensions. This tongue-in-cheek paper pokes fun at the peer review process; however, the response from survey participants suggests that the process is not necessarily funny. Peer review is essential for advancing science; however, for these advancements to occur, peer reviewer comments need to be constructive. The RIP matrix encourages both authors and reviewers to reflect on the impact of reviewer comments. This is essential because, as previous research has illustrated and as Voltaire succinctly stated, pain is real.
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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 | 0.168 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".