À l’écoute de Mishtamek Éthique collaborative et évaluation de la recherche en milieux autochtones Réflexion sur une expérience terrain
Bibliographic record
Abstract
L’un des principaux enjeux d’une démarche d’évaluation de la recherche tout comme de l’éthique collaborative est lié à la négociation du pouvoir. À l’écoute de Mishtamekͧ propose le récit d’une expérience terrain qui a consisté à mettre en œuvre une éthique collaborative commune au sein d’une équipe que nous avons formée aux fins de l’évaluation de certaines activités d’une programmation de recherche. Dans ce contexte, notre équipe a aussi expérimenté un processus d’évaluation collaborative en territoires innu et atikamekw. Ce récit de terrain s’accompagne d’une réflexion souhaitant contribuer à dégager quelques-uns des enjeux et défis propres à l’éthique collaborative dans le contexte d’une démarche d’évaluation de la recherche en milieux autochtones. Cette expérience nous aura permis de prendre encore plus conscience de la part d’évaluation impliquée dans l’éthique collaborative et du potentiel d’une entente de collaboration comme outil d’évaluation en recherche. Par une approche visant à créer des conditions propices à la réalisation d’un bilan évaluatif à plusieurs voix qui soit bénéfique pour l’ensemble des acteurs, est-il possible de rééquilibrer la balance du pouvoir? One of the main issues facing the evaluation of research—as well as collaborative ethics—involves the negotiation of power. Heeding the Voice of Mishtamekͧ describes our field experience, which consisted of implementing shared collaborative ethics in a team we formed to evaluate certain research activities. In this context, our team also experimented with a collaborative evaluation process in Innu and Atikamekw territory. This field experience story is complemented by our reflection on the manner in which some of the issues and challenges specific to collaborative ethics in the evaluation of research in Aboriginal contexts may be identified. This experience allowed us to gain more insight into the role of evaluation in collaborative ethics and the potential of a collaboration agreement as a research evaluation tool. Through an approach aimed at creating conditions favourable to the realization of a multi-voice evaluation process equitable to every actor, is it possible to rebalancing the scales of power?
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".