Narrative and the Reconfiguration of Social Work Ethics
Bibliographic record
Abstract
Commencing with a critique of codes of ethics based on the Statement of Principles of the International Federation of Social Work, we explore how a narrative approach to ethics might better serve the practice of social work. We argue that narrative both addresses some of the problems within current codes—such as their Western assumptions, lack of attention to the political role of the social worker, and the privileging of professional expertise—and aligns well with the values social work, being committed to social justice and diversity. Furthermore, we suggest that narrative, because it can operate at the individual, family, community, social, and discoursal levels can help us think ethically about how we construct narratives about, with, and for individual service users while remaining attentive to wider concerns of social justice. In so doing we are not seeking to construct a new code of ethics but to generate debate as to how social work ethics might be reconfigured.
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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 | 0.054 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.148 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| 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".