Bibliographic record
Abstract
A stereotype is a generalization that protects itself from critique by limiting or distorting new information. The potential for stereotyping exists where faculty members repeatedly rate students' clinical competency. Stereotyping is difficult to study because of methodological problems. If, for example, a student's score remains low over repeated ratings, it may be because the faculty member has pegged the student as a poor performer or because the student is in fact a consistently low performer. An existing dataset of clinical competency ratings for almost 300 students was divided so that ratings given by faculty members who had evaluated students previously could be compared to ratings of the same students by faculty members who had not previously evaluated these students. This study supports the following conclusions: 1) repeating faculty members use both current information and carryover information from previous rating periods; 2) the amount of information carried over increases from quarter to quarter; and 3) faculty evaluators who use more carryover information are more accurate in predicting students' graduation competency level than are faculty members on their initial ratings of students. In conclusion, there is no evidence in this study that repeated clinical competency ratings promote stereotyping of students by faculty members.
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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 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".