The Interacting Axes of Environmental, Health, and Social Justice Cumulative Impacts: A Case Study of the Blueberry River First Nations
Bibliographic record
Abstract
We consider the case of intensive resource extractive projects in the Blueberry River First Nations in Northern British Columbia, Canada, as a case study. Drawing on the parallels between concepts of cumulative environmental and cumulative health impacts, we highlight three axes along which to gauge the effects of intensive extraction projects. These are environmental, health, and social justice axes. Using an intersectional analysis highlights the way in which using individual indicators to measure impact, rather than considering cumulative effects, hides the full extent by which the affected First Nations communities are impacted by intensive extraction projects. We use the case study to contemplate several mechanisms at the intersection of these axes whereby the negative effects of each not only add but also amplify through their interactions. For example, direct impact along the environmental axis indirectly amplifies other health and social justice impacts separately from the direct impacts on those axes. We conclude there is significant work still to be done to use cumulative indicators to study the impacts of extractive industry projects-like liquefied natural gas-on peoples, environments, and health.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".