Model Victims of Hate: Victim Blaming in the Context of Islamophobic Hate Crime
Bibliographic record
Abstract
Prior research has explored victim blaming in the context of hate, often depicting hate crime victims as relatively passive recipients of harassment and violence. In reality, victims often do engage with their perpetrators, and the present research explored the effect that victim behavior might have on observer reactions to Islamophobic hate crimes. Participants completed a measure of Islamophobia and read a scenario in which a White man verbally harassed a victim in the park before physically assaulting him. We manipulated both the victim's identity (White or South Asian Muslim) and the victim's response to the perpetrator's verbal harassment (the victim either ignored the offensive comments, verbally reacted to them, or became physically confrontational). When the victim was portrayed as passive and nonresponding, the South Asian Muslim victim attracted lower victim blame, higher perpetrator blame, and increased certainty that the offense was a hate crime. As the victim's behavior became more aggressive, victim blaming increased and perpetrator blaming decreased, but only for the South Asian Muslim victim. It appeared that observers scrutinized the behavior of the South Asian Muslim victim in a way they did not for the White victim, such that sympathy toward the Muslim hate crime victim was tied to his "good behavior." We propose that observers hold expectations of the model hate crime victim, one who is a racialized, religious, or sexual minority who accepts harassment passively and with good behavior; deviation from this script results in a loss of sympathy and an increase in victim blaming. Finally, those higher in Islamophobia displayed reduced perpetrator blame, guilt, and sentences but greater victim blame when the crime targeted a South Asian Muslim as opposed to White victim.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".