Environmental justice in Canada: An application to a First Nations' struggle to protect caribou from coal mining in northeast British Columbia.
Bibliographic record
Abstract
In the United States of America, the field of environmental justice has become an important consideration in land use planning and natural resource management decisions regarding the protection of minorities. Within Canada, however, the field of environmental justice is not part of the legislative or policy regime used in environmental decision making. The focus of this study was to incorporate environmental justice into a situation in Canada involving a First Nation and a land and natural resource conflict. A phenomenology study design and a content analysis of the existing data were used to develop and apply the equality framework to a recent land use conflict between West Moberly First Nations and the Provincial Government of British Columbia. The results demonstrated that environmental justice can be incorporated into a Canadian context. When applied to the land use conflict, the equality framework demonstrated that the decisions made by the government to permit a coal mining company to destroy the critical habitat of a threatened herd of caribou were tantamount to an environmental injustice for the First Nation. The study concludes by discussing the differences of environmental justice as developed America in comparison to Canada, the challenges that associated with incorporation, and potential future applications and frameworks. --P. 2.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".