Bibliographic record
Abstract
In this in-depth analysis of First Nations opposition to the oil sands industry, James Heydon offers detailed empirical insight into Canadian oil sands regulation. The environmental consequences of the oil sands industry have been thoroughly explored by scholars from a variety of disciplines. However, less well understood is how and why the provincial energy regulator has repeatedly sanctioned such a harmful pattern of production for almost two decades. This research monograph addresses that shortcoming. Drawing from interviews with government, industry, and First Nation personnel, along with an analysis of almost 20 years of policy, strategy, and regulatory approval documents, Sustainable Development as Environmental Harm offers detailed empirical insight into Canadian oil sands regulation. Providing a thorough account of the ways in which the regulatory process has prioritised economic interests over the land-based cultural interests of First Nations, it addresses a gap in the literature by explaining how environmental harm has been systematically produced over time by a regulatory process tasked with the pursuit of ‘sustainable development’. With an approach emphasizing the importance of understanding how and why the regulatory process has been able to circumvent various protections for the entire duration in which the contemporary oil sands industry has existed, this work complements existing literature and provides a platform from which future investigations into environmental harm may be conducted. It is essential reading for those with an interest in green criminology, environmental harm, indigenous rights, and regulatory controls relating to fossil fuel production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".