Bibliographic record
Abstract
Sexual assault is a common experience, with nearly 460,000 occurrences happening each year in Canada. Research suggests that women attending university are sexually assaulted at a higher frequency than the general population. Sexual assault (SA) has wide-ranging harmful physical, financial, social, and psychological impacts. Prevalence rates of SA are higher among women than men (30% versus 8%). Given the prevalence rates of SA, there is an urgent need for more research into the experiences of sexual assault, particularly in terms of knowledge from survivors of sexual assault on campus. A narrative research method was chosen for this study because it affords survivors of sexual assault the opportunity to construct a personal narrative and to give voice to their experiences. The research question for this study was: “What narratives are constructed by survivors of sexual assault on campus?” All the survivors were current students at a university in British Columbia and had experienced a sexual assault within the past 5 years. Six narrative themes emerged: (a) difficulty considering the experience as sexual assault, (b) harmful emotional consequences, (c) a hesitation to report and disclose experiences, (d) a placating or freeze response, (e) a desire to reconnect with the perpetrator, and (f) the need for education, resources, and policy changes on campus. The findings have significance for university sexual assault policies and procedures and offer valuable information for sexual assault counsellors in their practice.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".