Global Warming as a Crime against Nature: Identifying the Principal Offenders
Bibliographic record
Abstract
This article employs a literature-based methodology, utilising green criminology literature and case examples to examine global warming through the framework of green criminology to assess whether contributions to global warming can be considered a ‘crime against nature’ due to their extensive environmental harm. Such environmental degradation aligns with the concept of ‘ecocide’, which criminalises acts that contribute to extensive damage or loss to the Earth’s ecosystems. The analysis identifies states and corporations as principal perpetrators, highlighting how fossil fuel industries contribute to global warming through environmentally detrimental practices and climate misinformation campaigns. Simultaneously, the actions of the state, such as obstructing climate change policies and appointing industry-affiliated personnel to key regulatory positions, exacerbate the climate crisis. While individual consumer behaviours are also contributory, these actions are largely constrained by the systems that are heavily influenced by state-corporate interests. Reframing global warming as a crime against nature highlights the urgent need for legal accountability and systemic reform to address the climate crisis. Recognition of ‘ecocide’ by the International Criminal Court would enable corporate and state actors to be held accountable for their harmful contribution to global warming.
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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 | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".