Bibliographic record
Abstract
The risk of sexual misconduct by forensic professionals appears at first glance to be far less than the risk of sexual misconduct by other clinical professionals. Yet, Faulkner and Regehr's article draws our attention to the unique and intriguing situation of females working in forensic settings and the very real risk of their engaging in sexual misconduct with male prisoners. The female workers described are professionals: nurses, prison staff, and security officers. Analogies are made between Gabbard's proposed categories of professionals who commit sexual boundary violations and groups of female forensic workers' sexual misconduct with male prisoners. Faulkner and Regehr detail the characteristics of prisoners and the prison setting and how they relate to detrimental interpersonal behavior by female forensic workers. The role of security officers is discussed along with the need for policy-makers to minimize the risks inherent in working with incarcerated populations. The potential for gender-biased explanations of misconduct among female forensic workers is also considered.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.216 | 0.082 |
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".