Anti-Oppressive Visual Methodologies: Critical Appraisal of Cross-Cultural Research Design
Bibliographic record
Abstract
The purpose of this article is to draw critical attention to the use of photovoice as an anti-oppressive method in research with Aboriginal peoples. In response to the historical vulnerability of Aboriginal peoples to research that “wants to know and define the Other,” anti-oppressive methods deconstruct taken-for-granted research models and focus on privileging Indigenous voices, political integrity, and justice strategies. Anti-oppressive approaches are connected to emancipation and cannot be divorced from the history of racism. Theoretically, photovoice aligns well with anti-oppressive goals, using photographs and storytelling as a catalyst for identifying community issues towards informed solutions. Having roots in Freireian-based processes, photovoice has the goal of engaging citizens in critical dialogues and moving people to social action. Drawing on our recently completed photovoice study, Visualizing Breast Cancer: Exploring Aboriginal Women’s Experiences (VBC), we demonstrate that photovoice seems successful in enhancing critical consciousness among participants, but that outcomes may not be disruptive. While photovoice has the potential to develop counter-hegemonic anti-oppressive knowledge, this may be lost depending on how the research process is encountered; thus, we propose the implementation of a revisionary model which incorporates a culturally safe anti-oppressive lens.
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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 | 0.697 | 0.743 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.024 | 0.014 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".