#WhyIDidntReport my sexual violence and its effect on social support
Bibliographic record
Abstract
An analysis of social media posts using the #WhyIDidntReport hashtag reveals six themes regarding the reasons why survivors of sexual violence do not report the incident to health or social organisations such as police or supervisors. Using just-world theory as a means to examine social reactions to posts of victim's stories, we suggest the reasons for not reporting could be divided into clusters of internal or external barriers. Within the first cluster, three themes reflect survivors who did not report because of external reasons (e.g. victim blaming by the police or other institutions; minimisation of the seriousness of the crime; and reporting costs). In the second cluster, three themes reflect survivors who did not report because of internal reasons (e.g. self-blame, protecting others, and naivety). We find that survivors who did not report sexual violence because of external reasons received significantly more social support, whereas survivors who did not report because of internal reasons received significantly less social support in the form of shares and likes. Overall, these findings support our theorising that the reasons why survivors do not report sexual violence are impactful because, consistent with just-world theorising, they change perceptions of victimhood and therefore the level of social support.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".