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Record W1975565019 · doi:10.1002/bmb.20592

Peer review in class: Metrics and variations in a senior course

2012· article· en· W1975565019 on OpenAlexaffabout
Krassimir Yankulov, Richard A. Couto

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClass (philosophy)Peer reviewPeer assessmentPeer evaluationTechnical peer reviewPeer feedbackPsychologyMedical educationPeer groupQuality (philosophy)Mathematics educationComputer scienceHigher educationSocial psychologyMedicineBiologyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.398
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2012
Admission routes2
Has abstractyes

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