The significance and impact of a faculty teaching award: disparate perceptions of department chairs and award recipients
Bibliographic record
Abstract
Teaching awards are commonly regarded as an incentive to encourage pedagogic excellence. Inasmuch as their effectiveness depends on how they are perceived by faculty, the authors investigated the impact of a teaching award in the Faculty of Medicine (Faculty Honor List for Educational Excellence) on the attitudes of award recipients and departmental chairs. A questionnaire was designed to sample opinion on the extent to which the Honor List program was publicized, whether the award contributed to recognition and/or stature in the academic unit, and whether it was personally valued by recipients. The questionnaire was sent to all 23 departmental chairs and to all 43 faculty members who had received the award between 1998 and 2002; 78% of the chairs and 77% of the recipients responded. The results revealed marked discrepancies between the perceptions of chairs and recipients. Chairs, although uncertain of the effect on quality of teaching, largely regarded the award as prestigious and well publicized within their departments. A notably smaller percentage of award recipients shared these views. Nonetheless, 93% of recipients valued the award highly, and 45% of recipients indicated that the award inspired them to enhance the quality of their teaching.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".