Bibliographic record
Abstract
The study of gender and crime has grown exponentially over the past 40 years, but in some fundamental respects, it remains underdeveloped. Few scholars have considered both the similarities and the differences in the predictors of offending among males and females and the implication of this for middle‐range theories. Victimization has been put forth as a major explanatory factor for female offending; yet the study of female victimization has been ghettoized because it has failed to address the ways in which it is related to the larger literature of victimization. Female inmates have always been characterized as having special needs, but the basic necessities (housing and employment) inmates require once they are released from prison are in fact gender neutral. These bodies of research all have suggested that the salience of gender varies in different contexts and is intermixed with other forms of stratification. As such, we would do well to attend to those situations and relational processes that foreground gender and focus our efforts on where gender‐based paradigms are important and can have a real impact.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".