Academic promotion packages: crafting connotative frames
Bibliographic record
Abstract
Among the challenges of navigating the promotion and tenure (P&T) process is the need to describe one's career using the language of P&T expectations, while also framing that language to reflect the unique work involved in health professions education (HPE) scholarship. Drawing on the distinction between denotative and connotative meanings of words, we describe how the language of P&T standards can hold different meanings depending on how they are contextualized in the HPE field and the communities therein. To illustrate, we describe our experiences of adapting the language of 'teaching' to the expectations of the P&T committee while also reflecting the non-traditional 'teaching' we do in HPE. We also share three practical tips for navigating the P&T process: (1) find a local mentor, (2) craft the story of your expertise, and (3) seek feedback from your local stakeholders on the connotative story you have crafted.
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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 | 0.056 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.076 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".