Understanding First Nations exposure and sensitivity to economic and ecological change in Canada
Bibliographic record
Abstract
First Nations in Canada engage in a form of mixed economic production that includes the complementary integration of subsistence (eg hunting, fishing, gathering, sharing) and wage-earning sectors. The flexibility of mixed economies has long enabled First Nations to optimise the use and allocation of household assets (eg time, labour, income) during times of economic and ecological change. In this study, we relied on the disaggregation of household (N=1268) data to measure the extent to which First Nations households in the Peace River region of British Columbia and Alberta engage in the mixed economy. We found that 24% (N=303) of First Nations households participate at an above average level in wage-earning and subsistence harvesting and are involved in relatively dense food sharing networks. These households are in the most optimal position to respond to economic or ecological changes by exploiting the range of household assets at their disposal. Conversely, 29% (N=368) of households participate in both wage-earning and subsistence harvesting at below average levels and are largely excluded from food sharing networks. These households may be most sensitive to even modest exposure. The results of this research offer a fine scale analysis of household characteristics that can be used by First Nations governments for targeted interventions to alleviate household exposure to economic and ecological change. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".