Don't tell it like it is: Preserving collegiality in the summative peer review of teaching
Bibliographic record
Abstract
While much literature has considered feedback and professional growth in formative peer reviews of teaching, there has been little empirical research conducted on these issues in the context of summative peer reviews. This article explores faculty members’ perceptions of feedback practices in the summative peer review of teaching and reports on their understandings of why constructive feedback is typically non-existent or unspecific in summative reviews. Drawing from interview data with 30 tenure-track professors in a research-intensive Canadian university, the findings indicated that reviewers rarely gave feedback to the candidates, and when they did, comments were typically vague and/or focused on the positive. Feedback, therefore, did not contribute to professional growth in teaching. Faculty members suggested that feedback was limited because of the following: the high-stakes nature of tenure, the demands for research productivity, lack of pedagogical expertise among academics, non-existent criteria for evaluating teaching, and the artificiality of peer reviews. In this article I argue that when it comes to summative reviews, elements of academic culture, especially the value placed on collegiality, shape feedback practices in important ways.
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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 | 0.289 | 0.624 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".