The Gaataa’aabing Visual Research Method: A Culturally Safe Anishinaabek Transformation of Photovoice
Bibliographic record
Abstract
Photovoice is a community-based participatory visual research method often described as accessible to vulnerable or marginalized groups and culturally appropriate for research with Indigenous peoples. Academic researchers report adapting the photovoice method to the sociocultural context of Indigenous participants and communities with whom they are working. However, detailed descriptions on cultural frameworks for transforming photovoice in order for it to better reflect Indigenous methodologies are lacking, and descriptions of outcomes that occur as a result of photovoice are rare. We address the paucity of published methodological details on the participant-directed Indigenization of photovoice. We conducted 13 visual research group sessions with participants from three First Nations communities in Northern Ontario, Canada. Our intent was to privilege the voice of participants in a mindful exploration aimed at cocreating a transformation of the photovoice method, in order to meet participants’ cultural values. Gaataa’aabing is the Indigenized, culturally safe visual research method created through this process. Gaataa’aabing represents an Indigenous approach to visual research methods and a renewed commitment to engage Indigenous participants in meaningful and productive ways, from the design of research questions and the Indigenization of research methods, to knowledge translation and relevant policy change. Although Gaataa’aabing was developed in collaboration with Anishinaabek people in Ontario, Canada, its principles will, we hope, resonate with many Indigenous groups due to the method’s focus on (1) integration of cultural values of the respective Indigenous community(ies) with whom researchers are collaborating and (2) placing focus on concrete community outcomes as a requirement of the research process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.037 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".