Teaching undergraduates the process of peer review: learning by doing
Bibliographic record
Abstract
An active approach allowed undergraduates in Health Sciences to learn the dynamics of peer review at first hand. A four-stage process was used. In stage 1, students formed self-selected groups to explore specific issues. In stage 2, each group posted their interim reports online on a specific date. Each student read all the other reports and prepared detailed critiques. In stage 3, each report was discussed at sessions where the lead discussant was selected at random. All students participated in the peer review process. The written critiques were collated and returned to each group, who were asked to resubmit their revised reports within 2 wk. In stage 4, final submissions accompanied by rebuttals were graded. Student responses to a questionnaire were highly positive. They recognized the individual steps in the standard peer review, appreciated the complexities involved, and got a first-hand experience of some of the inherent variabilities involved. The absence of formal presentations and the opportunity to read each other's reports permitted them to study issues in greater depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".