Bibliographic record
Abstract
Helen Jones is a Principal Lecturer in Criminology at Manchester Metropolitan University. Helen’s research and teaching interests include the politics of gender violence, critical analysis of policy on rape and sexual violence, and educational pedagogy. Her book Rape Crisis: Responding to Sexual Violence (with Kate Cook) was published by Russell House in 2008. She has written chapters in a number of books and is a member of the editorial panel of the journal Enhanced Learning in the Social Sciences. Helen has been a consultant on a number of government committees, including the Sexual Offences Review, which culminated in the enactment of the Sex Offences Act 2003. She has published work in journals such as Social Policy and Society, Feminist Media Studies and Contemporary Issues in Law. She has contributed to the Encyclopaedia of Victimology and Crime Prevention published by Sage in 2010 and has presented papers at numerous academic conferences in the UK, USA, Canada, Portugal, Sweden, Finland and Mongolia. Helen considers herself to be ‘so lucky to have worked with so many fine people, from research and writing partners, my students and colleagues, to my local rape crisis group and the women supported there. Yes, the work can be really tough but there is also much laughter and support. Whether we are writing articles or taking part in demonstrations, it all helps to move us forward and speak out.’
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".