“I’m a Monster, but I’m Not a <i>Monster</i> ”: Symbolic and Social Identity Work Among Child Sexual Exploitation Material Users
Bibliographic record
Abstract
Child sexual exploitation material (CSEM) users elicit strong negative reactions from society and people within their networks. There are symbolic and social boundaries that these individuals have transgressed, and subsequent identity work involves the negotiation of self and self-presentation. This article combines results from two studies to explore negotiation of identity, symbolic and social boundaries, and associated narratives among 103 CSEM users. One study was an anthropological ethnography with 17 months of UK fieldwork in community-based group programs, and the other involved four months of interviews in sexual offense treatment units of a US prison. Participants' identity work had commonalities: distinguishing between acts vs identities; differentiating crimes from identities; comparing offenses to others viewed as worse; framing childhood experiences as influencing offending; and situating both offending and post-offending identities within larger society. Results are discussed in the context of debates about risk, treatment, prevention, harm, denial/downplay/minimization, and reintegration. Furthermore, we highlight how identity work occurs within potentially competing/contrasting personal, judicial, treatment, media, and societal reactions to and expectations of individuals who have committed sexual offenses. Finally, we demonstrate the methodological and analytical value of cross-disciplinary comparative qualitative research by showing similarities across participants from different countries, settings, timeframes, and interventions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".