Bibliographic record
Abstract
Peer evaluation has been criticized as a threat to academic freedom and has been used, usually erroneously, as a justification for academic reappointment, tenure promotion, and merit pay. In recent years, scholars have recommended that peer consultation, which is primarily designed to improve teaching, be honored, but apart from evaluation.In this study, peer consultation consists of three components: in-class peer observation, peer-to-peer discussion, and student input. This study explores how teachers and teaching assistants perceived their experience with peer consultation. Methods of data collection reflected a qualitative case study approach and included participant observations, audio-recorded interviews, student questionnaires, and focus groups. Data showed that peer consultation provided an opportunity for teachers to learn teaching strategies from each other, build upon each other’s teaching, reflect on their own teaching experience, and augment their understanding of their teaching beliefs. Despite these benefits, teachers reported feeling uncomfortable because of the power relationship between the observer and the observed, and new teachers were apprehensive about their perceived lack of experience. Non-native English speaking teachers also felt anxious when they were observed by native English speaking teachers. Analysis shed useful light on implementation of peer consultation as a powerful professional development force for academic staff in universities.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".