Inequity of Exposure to Unconventional Natural Gas Development in Northeastern British Columbia, Canada─An Environmental Justice Analysis
Bibliographic record
Abstract
In the past two decades, northeastern British Columbia, Canada, has experienced rapid growth in unconventional natural gas production and is home to Indigenous and rural communities. Living near oil and gas production can lead to deteriorated air quality that negatively impacts human health. This study explores whether three oil- and gas-related exposure metrics: modeled concentrations of 12 gases and particles; oil and gas facility-reported emissions; and active wells are disproportionately distributed in areas with higher concentrations of Indigenous people and community socioeconomic vulnerability. We calculated exposure metrics from 2018 to 2020 in geographic dissemination areas (DAs). We used a rural deprivation index that included income, education, employment, and access to amenities to identify areas of high socioeconomic vulnerability. We estimated that DAs with greater than 90% Indigenous population experience 1.2-1.8 times higher median air pollution concentrations than DAs with less than 10% Indigenous population. We estimated that DAs with high community vulnerability experience higher modeled air pollution and higher odds of exposure to facility emissions, with the most vulnerable areas experiencing 11-96 times higher median air pollution concentrations. Overall, these results suggest the presence of environmental injustice in an area that is expected to continue producing a large portion of Canadian natural gas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".