Exited Prostitution Survivor Policy Platform
Bibliographic record
Abstract
Survivors of prostitution propose a policy reform platform including three main pillars of priority: criminal justice reforms, fair employment, and standards of care. The sexual exploitation of prostituted individuals has lasting effects which can carry over into many aspects of life. In order to remedy these effects and give survivors the opportunity to live a full and free life, we must use a survivor-centered approach to each of these pillars to create change. First, reform is necessary in the criminal justice system to recognize survivors as victims of crime and not perpetrators, while holding those who exploited them fully responsible. Second, reform is necessary to assist survivors in finding fair employment by offering vocational training, financial counseling, and educational scholarships, as well as offering employment opportunities that utilize survivors’ vast array of skills and interests. Finally, standards of care for survivors exiting prostitution should focus on supporting survivors in our journeys and support short- and long-term resources that empower us. These systemic changes are necessary to recognize survivors as the valuable human beings we are and to support survivors in fulfilling our vast potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.058 | 0.006 |
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".