Predicaments of the Academic Community-Based Educator
Bibliographic record
Abstract
Many academic educators who hope to contribute to socially just community building recognize that working in partnerships with people and organizations outside the academy is important. Working within publicly funded universities and engaging in community‑based work, however, require juggling responsibilities to multiple stakeholders. In this article, we present reflections on predicaments we encounter as academic community‑based educators as we balance obligations, relationship responsibilities, and outcome expectations that affect our work. We overview our educative projects and share examples of predicaments we encounter. We offer preliminary interpretations of what predicaments tell us about how power plays upon our practice and views of scholarship that may help us disrupt it. We contend that broad and inclusive perceptions of scholarship are required to allow space for community‑based educators and their partners to critically engage in community building that supports all society.
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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 | 0.062 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.070 | 0.114 |
| Scholarly communication | 0.034 | 0.020 |
| Open science | 0.005 | 0.055 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".