Enabling Community Well-being Self-Monitoring in the Context of Mining: The Naskapi Nation of Kawawachikamach
Bibliographic record
Abstract
The Naskapi Nation of Kawawachikamach is an Aboriginal community located in northern Quebec near the Labrador Border. Given the region’s rich iron deposits, the Naskapi Nation has considerable experience with major mineral development, first in the 1950s to the 1980s, and again in the past decade as companies implement plans for further extraction. This has raised concerns regarding a range of environmental and socio-economic impacts that may be caused by renewed development. These concerns have led to an interest among the Naskapi to develop a means to track community well-being over time using indicators of their own design. Exemplifying community-engaged research, this paper describes the beginning development of such a tool in fall 2012—the creation of a baseline of community well-being against which mining-induced change can be identified. Its development owes much to the remarkable and sustained contribution of many key members of the Naskapi Nation. If on-going surveying is completed based on the chosen indicators, the Nation will be better positioned to recognize shifts in its well-being and to communicate these shifts to its partners. In addition, long-term monitoring will allow the Naskapi Nation to contribute to more universal understanding of the impacts of mining for 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".