Research into prostitution in Northern Ireland
Bibliographic record
Abstract
approximate number and demographics of sex workers in Northern Ireland, including on-street and off-street prostitution; --the characteristics of the current sex industry, i.e. street-based prostitution and indoor prostitution;--an analysis of pathways into prostitution, reasons to sell sexual services and sex workers' experiences of prostitution;--the impact of prostitution on local communities;--approximate number and profile of victims of trafficking for sexual exploitation;--an analysis of the demand side of prostitution in Northern Ireland, including the approximate number of people who pay for sexual services, the demographics of clients, and their reasons to pay for sex;--an analysis of how clients in Northern Ireland access prostitution;--an assessment of the potential effects of criminalisation of paying for sexual services on a) those engaged in prostitution; b) those who pay for sexual services, and more generally the demand for prostitution, and c) the levels of sex trafficking;--an assessment of the existing support services for sex workers in Northern Ireland and additional services needed;--an assessment of programmes and services that support people in exiting prostitution in this and other jurisdictions;--an analysis of the effectiveness of the responses to prostitution in other jurisdictions;--an analysis of measures and programmes in other jurisdictions that aim to reduce demand for prostitution by non-legislative means.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".