Spatializing oil and gas subsidies in endangered caribou habitat: Identifying political‐economic drivers of defaunation
Bibliographic record
Abstract
Abstract Reforming environmentally harmful subsidies is an international priority under the UN Convention on Biological Diversity. Research that links industrial subsidies to negative ecological impacts, however, is limited. This paper contributes to the emerging agenda of global “subsidy accountability” research by linking oil and gas subsidies to the decline of endangered caribou herds in British Columbia, Canada. While existing research concretely attributes the decline of caribou herds to industrial activity, including oil and gas development, we suggest there is a need to identify the political‐economic structures which drive ongoing industrial development in caribou habitat, including public subsidies. We use government data to map oil and gas wells in critical caribou habitat and determine how many are run by operators receiving provincial fossil fuel “royalty credits”. Ultimately, we find that 1678, or 54%, of oil and gas wells located within critical caribou habitat are run by companies that have received benefits from one or both of BC's largest royalty credit programs. This paper points to the need for further analysis of subsidies as indirect drivers of biodiversity loss on a global scale, as well as increased emphasis on political‐economic drivers in conservation research. It also highlights the obstacles to implementing appropriate conservation solutions in political‐economic contexts dominated by resource extraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".