Understanding animal (ab)use: Green criminological contributions, missed opportunities and a way forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While the last two decades have witnessed considerable growth in green criminology, the positioning of nonhuman animals within the field remains unclear and contested. This article provides an analysis of green criminological work—published since the 1998 special issue of Theoretical Criminology—that addresses harms and crime perpetrated against nonhuman animals. We assess trends in the quantity of the work over time and how the treatment of nonhuman animals has unfolded through an analysis of green criminology articles, chapters in edited volumes and monographs. We find that while the amount of consideration given to nonhuman animals by green criminologists has increased dramatically over the years, much of this work has focused on crimes and harms against wild animals (e.g. “wildlife poaching”, “trafficking”), comparatively less attention has been paid to so-called “domesticated animals” or to larger questions of species justice. Based on these findings, we consider how concepts in critical animal studies, ecofeminism and feminist intersectional theories may be utilized in green criminological debates regarding animal (ab)use. With the goal of stimulating further work in this vein, we outline three areas where green criminology has much to offer: (1) researching and exposing meat production and consumption as a form of animal abuse and as a major contributor to global climate change; (2) bridging the divide between environmentalism, animal advocacy and their associated areas of academic study; and (3) refining and reflecting on methodological choices, all with the aim of developing a nonspeciesist green criminology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it