Bibliographic record
Abstract
Paula Barata is an Associate Professor in Applied Social Psychology. Her research deals with the psychosocial determinants that influence women's health and well-being. She works with community partners and academic colleagues to improve programs and policies for women who have experienced violence and to reduce violence against women on university campuses. She is currently collaborating with Associate Professor Ian Newby-Clark at the University of Guelph on the Sexual Assault Resistance Education Program being conducted at the University of Guelph with first year female undergraduate students. For more information about Paula Barata’s research, please go to her website at http://www.uoguelph.ca/psychology/page.cfm?id=707 \nStephen P. Lewis is an Associate Professor in Applied Social Psychology. His research examines self-injury material on the Internet. This research has resulted in the development of the Self-injury Outreach & Support (SiOS), the first international outreach initiative for self-injury (www.sioutreach.org). SiOS has been accessed in over 120 countries to date and provides reliable information for those who self-injure and those who can play a supportive role in recovery, including families, peers, partners, schools, and health professionals. For more information about Stephen P. Lewis’ research, please go to his website at http://www.uoguelph.ca/psychology/page.cfm?id=811
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.276 | 0.126 |
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".