"IT'S NOT RAINBOWS AND UNICORNS": REGULATED COMMODITY AND WASTE PRODUCTION IN THE ALBERTA OILSANDS
Bibliographic record
Abstract
This dissertation examines the regulated oilsands mining industry of Alberta, Canada, widely considered the world’s largest surface mining project. The industrial processes of oilsands mining produce well over one million barrels of petroleum commodities daily, plus even larger quantities of airborne and semisolid waste. The project argues for a critical account of production concretized in the co-constitutional relations of obdurate materiality and labor activity within a framework of regulated petro-capitalism. This pursuit requires multiple methods that combine archives, participant observation, and semi-structured interviews to understand workers’ shift-to-shift relations inside the “black box” of regulated oilsands mining production where materiality co-constitutes the processes and outcomes of resource development and waste-intensive production. Here, the central contradiction pits the industry’s colossal environmental impact and its regulated environmental relations, which – despite chronic exceedances – are held under some control by provincial and federal environmental agents, further attenuated by firms’ selective voluntary compliance with global quality standards as well as whistleblowers and otherwise “troublesome” employees. ‘It’s not rainbows and unicorns,’ explains one informant, distilling workers’ views of the safety and environmental hazards they simultaneously produce and endure as wage laborers despite pervasive regulation. In addition to buttressing geographical conceptualizations of socionatural resource production, contributions arise in the sympathetic engagement with workers, which may hold useful insights for activism against the industry’s environmental outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".