Fostering a Personal-is-Political Ethics: Reflexive Conversations in Social Work Education
Bibliographic record
Abstract
This article suggests that interviews about past ethical dilemmas or transgressions can foster ethical skills for navigating interlocking power relations. It shows how narratives claiming relative innocence are widespread and that taking responsibility for personal implication in oppression is crucial for fundamental social and political transformation. Chris is instructor and creator of a social work ethics course; Nazia and Louise are former students. In the second half of the article, Nazia and Louise use their interviews from the class as illustrations of personal-is-political ethical reflexivity. The authors encourage the use of resonant processes in social work ethics education and other pedagogical contexts that politicize everyday ethical navigation.
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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 | 0.037 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.084 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".