Bibliographic record
Abstract
This work focuses on the Harper government’s energy and environmental policy. Specifically, how environmental policy was designed primarily with the goal of facilitating their energy policy objective of maximizing resource extraction. I outline the evolution of the Harper government’s environmental policy throughout their tenure, which includes the use of ineffective and deceptively communicated legislation designed to give the impression of action, and the systematic dismantling of environmental regulation. Also, I explain the Harper government’s relationship with the resource extraction industry. Industry was given exceptional access to government, as they had substantial influence over policy and collaborated on communications strategy. The government also marginalized science, scientists, and environmentalists through defunding, muzzling, destruction of scientific records, and more. Finally, I examine the ideological underpinnings that inform these relationships and the rationality they construct. I will demonstrate that the Harper government’s neoliberal populist orientation characterized their dichotomous conception of economy and environment, and therefore their environmental and energy policies, and their relationship with perceived allies and adversaries. Ultimately, the Harper government’s efforts to maximize resource extraction through dismantling environmental regulation not only failed, but also obstructed their goal.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".