Re-defining Environmental Harms: Green Criminology and the State of Canada’s Hemp Industry
Bibliographic record
Abstract
Green criminology has been developing for more than 20 years as a field of criminological inquiry that grapples with defining and exploring environmental harms. This perspective includes approaches that look beyond legally defined environmental crimes, highlighting permissible activities that cause environmental deterioration, such as clear-cutting of forests, and prohibited activities that benefit the environment, such as pedicabs. Extending the criminological gaze helps green criminology identify unacknowledged environmental harms. The article draws from postmodernist/poststructuralist concepts to work past merely defining actions as either harmful or harmless, highlighting the complexity of socio-ecological effects and the importance of extending the conceptual boundaries of harm. Canada’s experiences with industrial hemp provide a fitting example. The heavily regulated Canadian hemp industry offers an important case for investigating the impacts of social constraints that limit the industry’s capacity to benefit the environment. Qualitative interviews reveal negative public perceptions, over-restrictive regulatory requirements, and insufficient technological capabilities as important obstacles to a fuller realization of hemp’s environmental benefits. Informed by constitutive criminology, chaos criminology, and Halsey’s important critique, the article adds to postmodernist/poststructuralist developments in green criminology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.031 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".