Recherche décoloniale : Promouvoir relationnalité, réciprocité et réflexivité critique dans une équipe canadienne autochtone et allochtone
Bibliographic record
Abstract
INTRODUCTION: Public health research has often perpetuated historical power imbalances, and in some cases continues to do so today. Indeed, it can exploit marginalized communities without bringing them equitable benefits. This ongoing practice prioritizes the agendas of dominant powers, neglecting local knowledge systems and imposing Eurocentric solutions. OBJECTIVE: Our research investigates decolonizing methodologies within a Canadian team composed of Indigenous and non-Indigenous members. RESULTS: Drawing on the works of Smith and Kovach, we implemented four key decolonial research principles: fostering relationships based on trust, challenging Eurocentric structures, supporting Indigenous self-determination, and ensuring an ethical research space. Our approach emphasizes relationality, reciprocity, and critical reflexivity, aiming to mitigate power imbalances and promote equitable collaboration. We adopted strategies such as advancing reciprocal decision-making, aligning methodologies to Indigenous worldviews and ways of knowing, reflecting on roles and positionalities, and developing relational accountability. CONCLUSION: This paper highlights the challenges in integrating these decolonizing strategies, underscoring their importance in creating equitable research processes. Our findings contribute to the growing discourse on decolonizing research, providing insights into the practical application of these principles in a collaborative research environment.
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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 | 0.170 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.101 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".