Playing Defence: Early Responses to Conflict Expansion in the Oil Sands Policy Subsystem
Bibliographic record
Abstract
Abstract. This article examines how powerful policy actors defend themselves against opponents' strategies of conflict expansion through a case study on the oil sands of Alberta. In response to an escalation of criticism of its performance on environmental regulation and related issues, the government of Alberta has pursued a strategy of engaging in several multi-stakeholder consultations. We argue that in examining subsystem change, it is essential to go beyond an examination of formal institutional mechanisms to examine policy impacts. Thus far, despite a significant pluralisation of consultative mechanisms on the oil sands, there is little or no evidence of a shift in power away from pro-oil sands interests. This strategy of selective opening is designed to bolster the legitimacy of the policy process while maintaining control over decision rules and venues. Résumé. Cet article étudie le rapport de force et la stratégie de défense des acteurs politiques lorsqu'un conflit dégénère, comme cela s'est produit dans le dossier des sables bitumineux de l'Alberta. Devant une recrudescence des critiques à l'égard de sa performance au chapitre de la réglementation environnementale, le gouvernement de l'Alberta a adopté une stratégie qui consiste à effectuer des consultations avec plusieurs intervenants. Nous soutenons qu'en examinant les changements du sous-système, il est vital d'aller au delà de la simple étude des mécanismes institutionnels pour évaluer l'impact des politiques. En dépit de la pluralité des mécanismes de consultation mis en place, rien ne semble indiquer qu'une partie quelconque du pouvoir ait échappé aux acteurs de l'exploitation des sables bitumineux. Cette stratégie d'ouverture sélective est conçue pour renforcer la légitimité du processus politique tout en gardant le contrôle sur les prises de décision et les centres décisionnels.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".