Measuring What Counts to Advance Indigenous Self-Determination: A Case Study of the Nisga’a Lisims Government’s Quality of Life Framework and Survey
Bibliographic record
Abstract
Modern Treaties are presented as a means for improving the lives of First Nations, Inuit, and Métis peoples in Canada by providing specific rights, and negotiated benefits. However, the positive impacts of Modern Treaties on Indigenous well-being are contested (Borrows and Coyle 2017; Coulthard 2014; Guimond et al. 2013; Miller 2009; Poelzer and Coates 2015). Developing a more transparent, consistent, collaborative and contextual way of measuring well-being relevant to the cultural realities of Modern Treaty beneficiaries is an important step for generating comparative methods that could systematically demonstrate whether, and under what conditions, such agreements can effectively reduce socio-economic disparities and improve the quality of life of Indigenous communities. The authors first examine previous attempts at measuring Indigenous well-being, then reflect on well-being in relation to the Modern Treaty context. Subsequently, the authors provide an example from one Self-Governing Indigenous Government, the Nisga'a Lisims Government, to collect well-being data through the Nisga'a Nation Household Survey using a mixed quantitative-qualitative method developed through a culturally grounded and participatory approach.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".