Spectacles of prison visibility: Masculinities, punishment and social order in US screen prison drama 1995-2005
Bibliographic record
Abstract
The United States has emerged at the head of an international trend in penal expansion and punitive crime control (Garland, 1990; 2001; Wacquant, 1999). A spate of screen prison dramas has emerged on US television and in the cinema during the period from 1995 to 2005. These dramas are the object of analysis in this dissertation. The project takes popular culture as site of negotiation over meaning and performs textual analysis of six recent screen prison dramas, highlighting play of resistive and normative discourses. Foreground are articulations of race and gender identities, discourses on crime and criminality, framings of sexuality and images of the prison institution. The study contends that neo-liberal discourses associated with punitive shift in criminal policy are refracted through norm-contesting discourses on sexuality, race and the prison system in screen prison drama. This perspective differs from research by criminology and sociology scholars (Mason, 1996; 1998a; 1998b; 2003; Nellis and Hale, 1982; Wilson and Sean O’Sullivan, 2004; Cheatwood, 1998). Criminological analyses of screen prison drama suggest that media texts “affect” penal policy conservatively (Mason, 1998a; 1998b; 2003; Nellis and Hale, 1982). Communications- and cultural studies writers Yvonne Jewkes (2005), Brian Jarvis (2004), Elayne Rapping (2003), and Nicole Rafter (2000) are limited also (with the exception of Jewkes), in relation to their somewhat binary analysis of the resistive/hegemonic facets of screen prison drama. Writers Rapping, Jarvis and Rafter focus solely on the normative aspects of screen prison drama, whereas criminologists Wilson and O’Sullivan (2004) are concerned to demonstrate resistive aspects of the genre. With reference to a Foucauldian perspective on social construction, and critical race writing, the project examines instances of prison visibility for discourses on crime and punishment, as well as indications of how other types of social formation are negotiated. The project situates itself also with theoretical debates on how the issues of race and social cleavages are mapped. Concern over the formation of social hierarchy thus meets a more strictly socially constructionist perspective on the processes of culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".