Increasing Cultural Competence in Support of Indigenous-Led Evaluation: A Necessary Step toward Indigenous-Led Evaluation
Bibliographic record
Abstract
Abstract: This paper builds on a keynote paper presented at the 2018 Canadian Evaluation Society annual conference by Nan Wehipeihana, an Indigenous (Māori) evaluator from Aotearoa New Zealand. Nan defines Indigenous evaluation as evaluation that is led by Indigenous peoples; has clear benefits for Indigenous peoples; has Indigenous people comprising most of the evaluation team; is responsive to tribal and community contexts; and is guided and underpinned by Indigenous principles, practices, and knowledge. She argues for Indigenous led as a key criterion for Indigenous evaluation, with no assumed or automatic role for non-Indigenous peoples unless by invitation. She outlines a range of tactics to support the development of Indigenous evaluators and Indigenous evaluation and presents a model for non-Indigenous evaluators to assess their practice and explore how power is shared or not shared in evaluation with Indigenous peoples, as a necessary precursor to increasing control of evaluation by Indigenous peoples.
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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 | 0.305 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".