Persistence and Proliferation: Integrating Community-Engaged Scholarship into 59 Departments, 7 Units, and 1 University Academic Promotion and Tenure Policies
Bibliographic record
Abstract
Choosing how to recognize community-engaged scholarship in promotion and tenure policies so that it is assessed accurately and fairly remains a relatively new and ongoing challenge for institutions of higher education. This case study examines how one U.S. research university integrated recognition of community-engaged scholarship across all levels of policy, including university, unit, and department. The terms used within and across policies reveal that while some terms were perpetuated across policies, many more terms proliferated across policies. Using organizational change and signaling theories, as well as the Democratic Civic Engagement Framework, analysis raises questions and insights regarding the use of both specificity and ambiguity when choosing and defining terms, and the use of terms across faculty roles of teaching, research/creative activity, and service to signal and address legitimacy of community-engaged scholarship within a larger context of institutional values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".