‘To Preserve and Promote’: Gendering Harm in Green Cultural Criminology
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.086 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".