Scholarship in Teaching
Bibliographic record
Abstract
At some medical schools broader definitions of scholarship have emerged along with corresponding changes in their academic reward systems. Such situations are not common, however. The definition of scholarship generally applied by medical schools is unnecessarily narrow and excludes areas of legitimate academic activity and productivity that are vital to the fulfillment of the school's educational mission. The authors maintain that creative teaching with effectiveness that is rigorously substantiated, educational leadership with results that are demonstrable and broadly felt, and educational methods that advance learners' knowledge are consistent with the traditional definition of scholarship. Faculty whose educational activities fulfill the criteria above are scholars and must be recognized by promotion. The authors specifically address scholarship in education, focusing on teaching and other learning-related activities rather than on educational research, which may be assessed and rewarded using the same forms of evidence as basic science or clinical research. They build on Boyer's work, which provides a vocabulary for discussing the assumptions and values that underlie the roles of faculty as academicians. Next, they apply Glassick et al.'s criteria for judging scholarly work to faculty members' educational activities to establish a basis for recognition and reward consistent with those given for other forms of scholarship. Finally, the authors outline the organizational infrastructure needed to support scholars in education.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".