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 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 | 0.005 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 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".