Cultivating a Racialized Practitioner Ethic: A Guide for the Human Services
Bibliographic record
Abstract
In this article, we ask critical questions and reflect upon ethics and praxis for racialized practitioners working alongside Indigenous communities in the human services field. Acknowledging the lack of scholarship across the human services focusing on experiences of racialized practitioners, we include literature from Indigenous and decolonizing studies, social work education, child and youth care (CYC), critical race studies, and women’s and gender studies. This article traces the journey of two racialized, women of colour practitioners in their development of a set of decolonial ethics for working alongside Indigenous peoples and communities. With a focus on the lived implications for racialized practitioners who face ongoing ethical dilemmas in their human services work alongside Indigenous peoples, we look to collaborative and critical conceptualizations for more just and ethical praxis. Core concepts explored include: notions of embracing risk, troubling allyship, and cultivating a decolonial love ethic.
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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 | 0.039 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".