MétaCan
Menu
Back to cohort
Record W4394822224 · doi:10.1177/25148486241243028

Critical environmental justice and the Wasteocene: Oppression and resistance in an Italian prison during the Covid-19 pandemic

2024· article· en· W4394822224 on OpenAlexaff
Elisa Privitera, David N. Pellow, Marco Armiero

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInjusticeFraming (construction)Environmental justiceOppressionCriminologyPrisonSociologyPolitical scienceEnvironmental ethicsLawGeographyPolitics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.329
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and Planning E Nature and SpaceSame topicEnvironmental Justice and Health DisparitiesFrench-language works237,207