Resource sterilization: reserve replacement, financial risk, and environmental review in Canada's tar sands
Bibliographic record
Abstract
Abstract. Pivoting on the process of reserve replacement undertaken by key oil transnationals in Canada as a spatial fix for capital, the article considers how individual firms employ formal review processes to project their strategic interests. The proponent firm shapes, through its own participation, the regulatory terrain on which competitors will subsequently operate. In Alberta's tar sands, the oil industry's reserve replacement process serves as a spatial–temporal fix for capital, and the review process and tribunal acts as a complementary socio-ecological fix – restricting social/affective claims, including First Nations resistance, to an official tribunal setting. In seeking formal approval to replace declining oil and gas reserves with unconventionals, proponent firms claim investor security, while social movement opponents emphasize risk and insecurity arising from carbon-intensive, frontier extraction. In the case of the contested Shell Jackpine Mine Expansion Joint Review Panel, as in other environmental assessment processes in Alberta, the proponent firm and state representatives employ the oxymoronic term ‘resource sterilization’ to describe ecological protection. ‘Resource sterilization’ offers a discursive representation of how capital's spatio-temporal fix in unconventionals is facilitated through the terms of the formal review process, in which social claims are muted.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".