Bibliographic record
Abstract
This paper explores racism specifically as an ethical concern in the field of social work and queries why it has been insufficiently emphasised in the discursive frames on ethics. The minimisation of racism as an ethical issue is illustrated utilising two research studies with racialised practitioners who highlighted experiences of racism. Epistemologies of ignorance by dominant groups contribute to norms that maintain dominance. These epistemological failings, as exemplified in social work, are delineated. Additionally, the utilisation of codes of ethics, based on the work of Kant, who was also an architect of a hierarchy of races, is considered. An exploration of this historical connection, and the traditional approach to ethics used in social work that followed, illuminates a difficulty with universal principles as they are interpreted in the Global North for primary guidance in social work ethics. The outcome of these problems results in testimonial and hermeneutic injustice for those affected by racism, causing significant harm.
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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 | 0.056 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.094 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".