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Record W4393956919 · doi:10.46692/9781529229646.015

‘To Preserve and Promote’: Gendering Harm in Green Cultural Criminology

2023· other· en· W4393956919 on OpenAlexaboutno aff
Angeline Marie Letourneau

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
Fundersnot available
KeywordsHarmGreen criminologyCriminologySociologyPolitical scienceLawCriminal justice

Abstract

fetched live from OpenAlex

Introduction Green criminology is an invitation to rethink how we conceptualise categories of harm and who the offenders or victims are (for example, Agnew, 2012; Sollund, 2017; Brisman and South, 2018). Accepting this invitation is no small feat. Feminist scholars have an established history of outlining the social and environmental repercussions of traditional, Western expressions of masculinities (Carson, 1962; Merchant, 1980; Plumwood, 1993; MacGregor, 2009). In this chapter I will take a cultural criminology approach to examine how masculinity serves to shape cultural understandings of harm while simultaneously justifying harmful activities associated with resource development in the oil and gas industry of the Canadian province of Alberta. I will illustrate how gendered cultural discourses ‘turn elite beliefs and values into common sense perceptions’ (Seiler and Seiler, 2004: 173–4) and foster public support for industrial development that is harmful both socially and environmentally. Cultural green criminology I begin this inquiry by considering what differentiates those harms which are criminalised under the law and those which scientific evidence would caution do have the potential to cause significant harm but are not yet criminalised. Cultural dynamics like the distribution of power within society determine the meaning of crime and, therefore, what harms will be criminalised (Ferrell et al, 2015). The distinction between political protest or civil disobedience, for example, is often a fine line. In many cases, scientific research recognises environmental harms that the law fails to capture (Lynch and Stretesky, 2001). In other situations, the law determines what counts as scientific evidence, often to the benefit of state interests (for example, Whitt, 2009). But as with the beginning of every apocalyptic film, the warnings of scientists often go unheeded, with environmental laws reflecting the economic interests that benefit from ecological withdrawals and additions (Stretesky et al, 2014). For example, ecological additions, like pollution, or the withdrawal of resources, are often framed as unfortunate but necessary costs of progress (Brulle and Pellow, 2006; Gould et al, 2008). Of course, this does not mean that environmental harms are never criminalised. After all, significant portions of both bureaucratic and corporate resources are devoted to navigating the immense regulatory processes meant to prevent unrestricted harm to the environment.Yet regulations rarely capture the full extent of harms, making trade-offs that require further scrutiny.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.223
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.086
Scholarly communication0.0120.004
Open science0.0010.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.125
GPT teacher head0.314
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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