Bibliographic record
Abstract
Image-based sexual abuse (IBSA) can be defined as the non-consensual sharing or threatening to share of nude or sexual images of another person. This is one of the first studies examining how demographic characteristics (gender, sexual orientation), personality traits (Dark Tetrad), and attitudes (aggrieved entitlement, sexual entitlement, sexual image abuse myth acceptance) predict the likelihood of engaging in IBSA perpetration and victimization. In a sample of 816 undergraduate students (72.7% female and 23.3% male), approximately 15% of them had at some point in their life, distributed and/or threatened to distribute nude or sexual pictures of someone else without their consent and 1 in 3 had experienced IBSA victimization. Higher psychopathy or narcissism scores were associated with an increased likelihood of having engaged in IBSA perpetration. Additionally, those with no history of victimization were 70% less likely to have engaged in IBSA perpetration compared to those who had experienced someone disseminating their intimate image without consent themselves. These results suggest that a cyclic relationship between IBSA victimization exists, where victims of IBSA may turn to perpetration, and IBSA perpetrators may leave themselves vulnerable to future victimization.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".