A Culturally Adaptive Approach to First Nations evaluation consulting
Bibliographic record
Abstract
Cultural safety is of utmost concern across the evaluation world, particularly given the way that evaluation and research have historically been implicated in colonising practices of the West. This article aims to examine the meaning of cultural safety in the context of an Aboriginal majority-owned consulting organisation that provides evaluation services to organisations where First Nations governance systems and processes may be unknown. This is a critically reflexive article that considers how the dual aims of contributing to self-determination and building First Nations business capacity may be managed in such evaluation projects. We apply Duke et al.’s Culturally Adaptive Governance Framework to our own evaluation work in striving for evaluations to be experienced as culturally safe by Aboriginal and Torres Strait Islander stakeholders and for evaluation outcomes to be relevant and useful from the perspective of both Aboriginal and Torres Strait Islander stakeholders and our clients. We then reflect on the implications for the evaluation, social policy and for First Nations business sectors.
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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 | 0.097 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".