Anti-Racist Research Praxis: Feminist Relational Accountability and Arts-Based Reflexive Memoing for Qualitative Data Collection in Social Work Research
Bibliographic record
Abstract
Largely absent from the feminist qualitative social work research literature are practical discussions about the ethics of white researchers who “study up” people and institutions of power. This methodological article grapples with how to conduct data collection from an anti-racist framework. I explore my use of an arts-based self-reflexive memoing process of embodied tableaux to inform my experimentations of rejecting “neutrality” when interviewing participants. I provide examples of disrupting white, patriarchal, and colonial norms during qualitative interviewing, including directly naming my whiteness and anti-racist stance; intentionally challenging the racism of white participants and deepening critical reflection; and viewing myself through a lens of critical skepticism to recognize when I was protecting whiteness or failing to effectively intervene. I conclude with an invitation to others to experiment with an anti-racist research praxis—an iterative process of self-reflexivity and relational accountability to reflect, theorize, and act differently during feminist social work research.
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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 | 0.340 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.131 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".