Surrendering the Night!: The Seduction of Victim Blaming in Drug and Alcohol Facilitated Sexual Assault Prevention Strategies
Bibliographic record
Abstract
This article explores, within the Australian context, the background of what is in fact a very old crime, a crime of sexual violence but performed using a different weapon, now known as the phenomenon of drink spiking, examine the systemic responses to the crime of drug and alcohol facilitated sexual assault. This article argues that women's entire lives are dictated by caution in our conduct, behaviour, actions and inactions, yet there is no evidence to suggest that sexual assault is on the decline. Why uphold the myth that lists of dos and don'ts restricting women's behaviour will cease to make them targets for rape (Vancouver Rape Relief and 38 Women Against Violence: Issue Thirteen 2002 - 2003 Women's Shelter)? As feminists and advocates of women's rights, do we really wish to support the individualising of the incidence of sexual assault? And finally, when did sexual assault stop being a gendered crime? So next time someone tells you to watch your drink, substitute it with the phrase don't wear a short skirt and see how comfortable you feel with this age old formula for blaming women.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| 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".