Gay Visibility and Disorganized and Strained Communities: A Community-Level Analysis of Anti-Gay Hate Crime in New York City
Bibliographic record
Abstract
Recent years have seen increased attention to the problem of hate crime, including such crime motivated by anti-gay bias. Although there is a growing body of research regarding the context of hate crime offending, there is a relative dearth of work investigating the community-level context of anti-gay hate crime. The current study investigates the community-level determinants of anti-gay hate crime in New York City from 2006 to 2010, using data obtained from the New York Police Department (NYPD)'s Hate Crimes Task Force (HCTF), one of the nation's leading hate crime police units. Using a framework drawing on group conflict and criminological theories, the current study examines anti-gay hate crime as an outcome of gay visibility, social disorganization, and economic strain. It is hypothesized that greater gay visibility, as well as social disorganization and poor and worsening economic conditions over time will be associated with increases in anti-gay hate crime. Results show that gay demographics, measured by static visibility and increasing gay populations over time, are shown to consistently predict higher levels of anti-gay hate crime. Adding to the generally mixed findings on the role of economic conditions in explaining hate crime, this study also finds that anti-gay hate crime occurs in more disadvantaged communities and communities marked by poorer economic conditions. The findings show anti-gay hate crime to be an outcome of gay visibility, disadvantage, and poor economic conditions, indicating that anti-gay crime may be an angry response to the strains present in the community. The study concludes with a discussion of the findings and implications for policy makers and practitioners.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".