Bibliographic record
Abstract
This chapter offers faculty and institutional leaders a set of principles and practical approaches for designing and supporting courses that develop and mentor emerging community engaged scholars at the undergraduate and graduate levels. The learning outcomes and design features of these courses provide students with opportunities to develop knowledge, skills, and values that are required for undertaking ethical sustainable critical community engaged scholarship (CCES). The chapter begins with an overview of the CCES framework that guides the authors' specific courses and thier commitments to supporting the development of community-engaged scholars more broadly. The chapter describes several courses that share the CCES framework but vary by size, disciplinary foundation, and engaged-learning approach. These courses are used to consider the development of students' capacities and values, the interplay between CCES and pedagogical best practices, and the role of institutional supports in enabling CCES and navigating institutional challenges to community-engaged teaching and learning.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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".