We’re Talking About Process: The Primacy of Relationship and Epistemology in Defining Community-engaged Scholarship in Promotion and Tenure Policy
Bibliographic record
Abstract
This study examines how community-engaged scholarship (CES) was defined and described in promotion and tenure policies at a community-engaged, high research activity university in the United States. Examining 67 policies across university, unit and department levels, findings show meaningful variability with regards to whether and how CES was defined or described. Analysis categorizes the types of descriptions used as outputs, outcomes, and/or processes related to or enacting CES. We address the importance of not just naming, but fully describing the core values of CES. We discuss the importance of describing the ways that presence of relationships and approaches to epistemology influence scholarly process and scholarship; and, the importance of continuing to explicate the interconnectivity and distinctions between CES and diversity, equity, inclusion, and epistemic justice.
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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 | 0.026 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.013 |
| 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".