Becoming a Teacher Scholar: The Perils and Promise of Meeting the Promotion and Tenure Requirements in a Business School
Bibliographic record
Abstract
Many business schools continue to use contribution in teaching, research, and service as measures of faculty performance. There has been a long tradition of thinking of faculty as making their research contribution within a specific subdiscipline. We call these teaching and discipline scholars (TDS). However a growing number of faculty who, although they teach in a subdiscipline, are choosing to make their research contribution in the teaching and learning area. We call these persons teaching and learning scholars (TLS). A major hurdle facing TLS candidates is a promotion and tenure (P&T) system primarily designed for teaching and discipline scholars. This article takes a granular look at the typical P&T system within business schools. It proposes a way of thinking about what is typically meant by teaching ability and how it might be measured. It then discusses what is meant by research, how this definition might be applied to measure the output of TLSs and the special challenges for TLSs in having their research accepted as part of their P&T portfolio. Suggestions are provided for how the TLS may navigate the P&T process in light of these challenges.
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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 | 0.047 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".