Peer review in class: Metrics and variations in a senior course
Bibliographic record
Abstract
Peer reviews are the generally accepted mode of quality assessment in scholarly communities; however, they are rarely used for evaluation at college levels. Over a period of 5 years, we have performed a peer review simulation at a senior level course in molecular genetics at the University of Guelph and have accumulated 393 student peer reviews. We have used these to generate a summary of the metrics of this exercise. Our calculations show that student peer marks are highly variable and not suitable for numerical performance evaluation at the university level. On the other hand, student peer reviews can clearly recognize substandard performance. Hence, peer reviews can be used for the assessment of "pass/fail" type of assignments. Interestingly, student peers struggle to distinguish between good and excellent performance. These finding provide provocative insight on the process of peer review in general. We comment on the implications of this in-class simulation for research communities and on potential pitfalls of peer reviews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".