Critical environmental justice and the Wasteocene: Oppression and resistance in an Italian prison during the Covid-19 pandemic
Bibliographic record
Abstract
This article aims to frame the state violence and socio-ecological injustice perpetrated against prisoners through the lens of both critical environmental justice studies and the concept of the Wasteocene. We seek to uncover the socio-ecological relationships that have historically shaped the enforcement of the prison and waste systems through a focus on the Italian context during the Covid-19 pandemic. We explore the case of a jail in Campania, a region in the South of Italy infamous for its troubled waste management that has caused uncountable and entangled health, social, and economic harms. The jail is adjacent to an area with a long history of waste disposal practices and numerous legal conflicts and corruption scandals: all characteristics that make this case emblematic of the broader problem of carceral environmental injustice. We argue that carceral institutions are generative sites for examining the dynamics of violence, expendability, and wasting relationships that are built into their structures and core functions We also maintain that the Covid-19 pandemic has both uncovered and exacerbated such dynamics and therefore stands as a framing device that further corroborates our argument. We conclude with lessons and observations for scholars studying environmental concerns and carceral systems through a multidisciplinary lens.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".