How can we reduce bias during an academic assessment reappraisal?
Bibliographic record
Abstract
Aims: To describe potential sources of bias during an academic assessment reappraisal and ways to mitigate these.Methods: We describe why the typical scenario of an academic assessment reappraisal – where committee members are asked to weigh contrasting accounts of past events that they did not witness, and to rate elusive constructs, such as “fairness” – is prone to multiple types of bias, including attribute substitution, default bias, confirmation bias, and impact bias. We also discuss how increased awareness of sources of bias and of debiasing strategies can improve the validity of decision making.Results: Strategies that can reduce bias in reappraisal include clearly articulating and focusing on the reappraisal question (did bias cause a wrong decision to be made?), educating those involved in the reappraisal of the types of bias that frequently occur in teaching and assessment (including biases that they themselves may introduce to the reappraisal), and ensuring that those involved in the reappraisal contribute equally to making decisions and recommendation.Conclusions: All academic assessments of students, particularly those that involve subjective ratings of performance, are prone to bias, which threatens the integrity of the assessment process. Given the high stakes of academic assessments, we feel that each medical school should have a process for assessment reappraisal that reduces, rather than compounds, the likelihood of wrong assessment decisions.
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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 | 0.419 | 0.660 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".