From voice to action: toward participatory action research in anti-racism education in Canada
Bibliographic record
Abstract
This study explores the transformative role of voice within participatory action research (PAR) as both a narrative and analytical tool for addressing systemic racism. PAR fosters critical engagement, reflexivity, and collaborative knowledge creation by amplifying marginalized voices to challenge entrenched power structures. This research investigates how the voice-giving methodology informs our anti-racism education program design, connecting personal narratives with structural critiques to expose and disrupt racial inequities. Utilizing Critical Race Theory (CRT) alongside PAR, our study integrates reflective and action-oriented approaches to examine intersectional dimensions of racism while fostering community-based collaboration. Findings emphasize PAR’s participatory, collective, and actionable impacts in anti-racism education, demonstrating how it transforms lived experiences into actionable insights. This approach enhances program design by grounding anti-racism efforts in inclusive, contextually relevant frameworks that empower participants, promote meaningful dialogue, and facilitate enduring social change across individual, institutional, and societal levels, advancing equity and justice-focused outcomes.
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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 | 0.051 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.033 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".