Basing the Evaluation of Professionalism on Observable Behaviors: A Cautionary Tale
Bibliographic record
Abstract
PROBLEM STATEMENT AND BACKGROUND: The evaluation of professionalism often relies on the observation and interpretation of students' behaviors; however, little research is available regarding faculty's interpretations of these behaviors. METHOD: Interviews were conducted with 30 faculty, who were asked to respond to five videotaped scenarios in which students are placed in professionally challenging situations. Behaviors were catalogued by person and by scenario. RESULTS: There was little agreement between faculty about what students should and should not do in each scenario. Abstracted principles (e.g., honesty, altruism) were defined and applied inconsistently, both between and within individual faculty. There was no apparent "shared standard" that faculty held for professional behavior in students, and similar behaviors (e.g., lying) could be interpreted as either professional or unprofessional. CONCLUSIONS: Future efforts at evaluation need to look beyond the behaviors, and should incorporate the reasoning and motivations behind students' actions in challenging professional situations.
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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 | 0.276 | 0.488 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.014 | 0.007 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".