Implementing Indigenous Gender-Based Analysis in Research: Principles, Practices and Lessons Learned
Bibliographic record
Abstract
Numerous tools for addressing gender inequality in governmental policies, programs, and research have emerged across the globe. Unfortunately, such tools have largely failed to account for the impacts of colonialism on Indigenous Peoples’ lives and lands. In Canada, Indigenous organizations have advanced gender-based analysis frameworks that are culturally-grounded and situate the understanding of gender identities, roles, and responsibilities within and across diverse Indigenous contexts. However, there is limited guidance on how to integrate Indigenous gender-based frameworks in the context of research. The authors of this paper are participants of a multi-site research program investigating intersectoral spaces of Indigenous-led renewable energy development within Canada. Through introspective methods, we reflected on the implementation of gender considerations into our research team’s governance and research activities. We found three critical lessons: (1) embracing Two-Eyed Seeing or Etuaptmumk while making space for Indigenous leadership; (2) trusting the expertise that stems from the lived experiences and relationships of researchers and team members; and (3) shifting the emphasis from ‘gender-based analysis’ to ‘gender-based relationality’ in the implementation of gender-related research considerations. Our research findings provide a novel empirical example of the day-to-day principles and practices that may arise when implementing Indigenous gender-based analysis frameworks in the context of 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 | 0.351 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.029 | 0.121 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".