Doing participatory evaluation in Indigenous contexts - methodological issues and questions
Bibliographic record
Abstract
In countering the legacies of colonisation, aboriginal communities across Canada are beginning to mount their own locally inspired and developed initiatives in business, health, welfare and education to address needs that they have identified. This paper reports on one such initiative created and launched by the Cree Nation of Wemindji (in Quebec, Canada), called COOL (Challenging Our Own Limits) or Nigawchiisuun. The paper briefly outlines the creation, development and implementation of COOL and the theoretical and methodological framework that supports the project. The paper is organized into three sections. First, a brief background and discussion of the origins, impetus and eventual launch of COOL; second, a general theoretical framework situating participatory evaluation (PE) in relation to the broader field of participatory action research (PAR); and third, the implications and potential of this methodology for indigenous research. The paper concludes with remarks on participatory evaluation as an indigenous alternative to mainstream program evaluation and related managerial technologies.
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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 | 0.555 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".