Bibliographic record
Abstract
Peer review is generally understood as the hallmark of scholarly communication, the point of distinction between serious, research-informed publishing and less-trustworthy, ephemeral, or even spurious sources. And yet, a close examination of peer review practices and the growing literature reveals peer review as a kind of black box concealing a tangle of differing rationales, spectres, and imagined standards, often mutually incompatible. Peer review is not monolithic and indeed scholarly societies and discourses would do well to come to a more explicit agreement about what they mean by peer review and what they exactly want it to do. In the context of open social scholarship as it has been defined by the INKE community, what do we want peer review to be, and to do for us? This essay proposes a version of peer review that places care and care ethics at the centre of its operations, serving a more generous model of scholarship that values people and relationships.
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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 | 0.117 | 0.605 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".