Corporate Power and Changes to Provincial Environmental Regulation During the First Year of the COVID-19 Pandemic
Bibliographic record
Abstract
How have Canada’s largest oil producing provinces altered key environmental policies since \nthe onset of COVID-19, in response to the dual pressures of an oil sector in distress and the \nimperative to reduce emissions? While regulatory changes have been reported in the media, they have \nnot yet been systemically reviewed or explained; this project aims to fill that gap. \n \nOil markets went into crisis in early 2020 as oil prices plummeted following an oil price war \nbetween Russia and Saudi Arabia and the economic downturn caused by the COVID-19 pandemic. \nMeanwhile, the global community has entered into a critical decade in climate history: the \nIntergovernmental Panel on Climate Change has stated that a sharp reduction in emissions over the \nnext decade is needed to avoid the worst consequences of climate change. Government policy \ninterventions in this moment are both determining the future of the oil sector and defining \npossibilities for climate change mitigation. \n \nThis thesis analyzes changes to regulations made by the oil-producing provinces of \nSaskatchewan and Newfoundland and Labrador at this critical moment. Conducting a full review of \nprovincial regulatory changes during the pandemic, I find that in the first year of the COVID-19 \npandemic Canada’s oil provinces demonstrated a clear pattern of supporting the oil sector by \nweakening provincial environmental regulation surrounding the sector. Regulatory changes observed \nin 2020 can be explained in part by considering corporate power, and strategies used by oil \ncorporations to influence government, in each province. These changes to provincial regulatory \nframeworks shape Canada’s response to the ongoing economic and climate crises, and further expose \nCanadians to both the risks of climate change and the economic risk of an oil sector in long-term \ndecline.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".