Girly mags and girly jobs: Pornography and gendered inequality in forensic practice
Bibliographic record
Abstract
This article presents findings from a discourse analytic study into the constructive nature and textual variations of language in a high-security hospital. It explores how mental health nurses, and men convicted of sexual offences who also have a diagnosis of personality disorder, talked about pornography and sexual crime in the context of forensic provision. Access to sexually-explicit media, in relation to treatment environments for people convicted of sexual offences, has become a cause for professional and political concern in the UK. Data collection and analysis, undertaken concurrently, were informed by a discursive design. Semistructured interviews, as co-constructed accounts with nursing staff and detained patients, were audio-taped and transcribed. Data were coded to identify the discursive repertoires, or collective talk, of respondents. In contrast to empirical inquiry into pornography and sexual violence, methodology shifted attention from measurement to meaning, and situated research in a clinical domain. The findings focus on performative language use, where talk about pornography textured the treatment environment, contributed to an overtly masculine discourse, framed the ward as male space, and promoted gendered inequality. The discussion questions the legitimacy of the therapeutic enterprise.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".