A Listening Guide Analysis of Women’s Experiences of Unacknowledged Rape
Bibliographic record
Abstract
In the empirical literature, unacknowledged rape has been well documented. But due to ethical and methodological challenges, very few researchers have employed qualitative methods to examine unacknowledged rape. Through pre-screening and careful articulation of interview questions, these barriers were overcome, and 10 undergraduate women from the University of Windsor were interviewed about their experiences of unlabeled sexual assault. I used the Listening Guide to inform both the methodology and the data analysis. I identified three voices pertaining to rape acknowledgment. These voices were labeled the not knowing voice, the knowing voice, and the ambivalent voice, and I illustrate that rape acknowledgment is not dichotomous and that women can both simultaneously recognize and resist the labels of rape and sexual assault. This article addresses the need for a multidimensional understanding of rape acknowledgment. I discuss the implications the findings have for how we understand and respond to women, as they negotiate the labeling of coercive sexual experiences. Online slides for instructors who want to use this article for teaching are available to PWQ subscribers on PWQ's website at http://pwq.sagepub.com/supplemental
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".