Sexual Assault and Sexual Harassment in Military Services: Culture, Hypermasculinity, and the Futility of Zero-Tolerance Approaches
Bibliographic record
Abstract
This introduction to the Special Forum on Sexual Assault and Sexual Harassment in Military Services begins with a discussion of zero-tolerance approaches to sexual assault and sexual harassment, arguing that such approaches tend to be ineffective and result in problematic outcomes. Furthermore, zero-tolerance approaches are associated with the same hypermasculine cultural practices that contribute to the prevalence of sexual assault and sexual harassment within the military. The articles that are summarized in this Forum cover a range of inquiries, which apply differing substantive, theoretical, and methodological approaches to address issues related to sexual assault and sexual harassment. Together, the articles in this compendium demonstrate the importance of aligning structural interventions with the cultures of the institutions in which the interventions will be implemented. They also reinforce the futility of zero-tolerance approaches and the importance of recognizing the significant complexities associated with sexual assault and sexual harassment in military services.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".