Generating prosperity, creating crisis: impacts of resource development on diverse groups in northern communities
Bibliographic record
Abstract
Abstract Northern Canada illustrates the contradictory dynamics in resource development – at once generating prosperity and inclusion within some communities and for some people, and creating or perpetuating crisis in some communities and exclusion for some people. Existing literature related to resource extraction and development focuses on the impacts on the environment and government regulatory mechanisms. Few authors or policy makers pay attention to how multiple and diverse groups within communities are affected by resource development. Building from research in a community-university research alliance, the authors argue that these competing dynamics are initiated and sustained through resource development projects and have disproportionate effects on historically marginalized groups within northern communities. This article presents the results of a comprehensive scoping review of the literature related to the social and economic impacts of resource extraction in Northern Canada. Some of the impacts of resource extraction clearly generate prosperity, while others can move communities towards crises and some do both. Using intersectionality, we argue that policy makers, especially those responsible for community development and regulating resource development projects, require a multilayered analysis to understand and redress the unequal effects of resource development on northern communities.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".