Nation-to-Nation Evaluation: Governance, Tribal Sovereignty, and Systems Thinking through Culturally Responsive Indigenous Evaluations
Bibliographic record
Abstract
Abstract: This paper was presented as part of the opening plenary panel at the 2018 Canadian Evaluation Conference in Calgary, Alberta, on May 27, 2018. Through telling the origin stories of First Nations/Indigenous people and Western evaluation colleagues, we can begin to understand the history and practical applications for advancing the truth through evaluation. The Doctrine of Discovery is rarely told as part of the Western canon of history or contemporary evaluation practice. There are significant and negative cultural, human rights, and social impacts that have deep institutional and systemic roots that continue to cause harm to First Nations/Indigenous populations throughout the world. To change centuries of old negative outcomes and impacts, we must understand our personal origin stories and the origin stories embedded within evaluation. Governance, policy, and evaluation can work as transformative levers for professional and sustained change if systems, critical and Indigenous theories, and methods are utilized. This paper offers origin stories of First Nations and colonial nations as a historical perspective and a new Tribal Critical Systems Theory to change contemporary Nation-to-Nation evaluation practices.
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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 | 0.095 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".