Changing the Perceptions: Tracing Feminist and Postfeminist Apprehensions of Rape Culture in Sohaila Abdulali’s What We Talk About When We Talk About Rape
Bibliographic record
Abstract
Rape is the linchpin of patriarchy. The prevailing perception of rape and rape victims is an important matter of discussion as many people still believe in rape myths. Writer, activist, and rape survivor Sohaila Abdulali’s personal narrative What We Talk About When We Talk About Rape questions gender roles and the patriarchal system. She describes how erroneous beliefs about rape, rapists, and rape victims affect society and leads to victim blaming, slut shaming, and questioning the behaviour of women. Rape myths, which are by-products of patriarchy, rationalise sexual violence and promote animosity toward the victims. In the book, Abdulali urges society to shift its focus from women as victims to men as rapists. By depicting the real incidents, the author shows how men use power to justify rape and sexual assault. Born in India, she takes the issue of rape to the global level by addressing rape cases from all around the world to show how rape affects people from various communities and cultures. This paper seeks to explore the ways in which Sohaila Abdulali deals with the issue of rape with reference to gender, race, and class. The paper also looks at how much society has changed over time in terms of its perceptions of rape and rape victims while there are many people who still adhere to the old gender stereotypes. The study draws on feminist and postfeminist theoretical elements to address the issue of rape.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".