A Proposal for the Political Economy of Green Criminology: Capitalism and the Case of the Alberta Tar Sands
Bibliographic record
Abstract
Green criminology was proposed in 1990 to broaden the discipline and illustrate how environmental crime, deviance, and inequality can be interpreted through a critical lens influenced by political economic theory. Green criminology has yet to fulfill that theoretical promise. Instead, the political economic perspective on green criminology remains underdeveloped. The purpose of this study is to contribute to further development of a political economic green criminology by laying out the connection between ecological Marxism and green criminology. To carry out this task, we describe five propositions that criminologists must consider when developing a green criminology from a political economic perspective. Importantly, these propositions suggest that the environmentally destructive forces of capitalism are opposed to nature. That is, we argue that green criminologists must come to recognize that capitalism and nature cannot both survive over the long run, and in criminological terms, capitalism is therefore a crime against nature.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".