"TRACKING THE INCIDENCE OF US HATE CRIMES BY KEY LEGISLATIVE MARKERS (1991-2020)"
Bibliographic record
Abstract
The Hate Crime Statistics Act (1990) in the US requires that the Attorney General collect and publish annually the incidence of hate crimes in the US based on a victim's race, religion, disability, sexual orientation, or ethnicity.With almost 220,000 hate crimes committed from 1991-2020, the present study tracked changes in the incidence of 163,375 racially-motivated hate crimes, as perpetrated against each of the following groups: Arabs, Asians, Blacks, Hispanic, Indigenous, Jews, and Whites.Crimes ranged from intimidation to homicide.Legislative markers included both widely publicized incidents (viz.James Byrd and Matthew Shepard, 1998), as well as significant changes to law enforcement and criminal punishment including the Violent Crime Control Act (1994), Church Arson Prevention Act (1996), Hate Crimes Prevention Act (2009); and most recently the Emmett Till Antilynching Act (2022).We tracked changes in crime rates by six 5-year time periods and region in the US.Initial analysis indicated changes in hate crime rates over time, but with a unique pattern for each racial group.For instance, hate crimes against both Asian and White victims were at their highest in 1991-1995, but steadily decreased despite a rise in 2016-2020.Crimes against Indigenous were lower in the first two decades only to increase in 2011-2020.Black victims (routinely with the highest incidence) saw a zigzag pattern: low in the early 90s, then high; low again in the early 2000s, then high; but lower from 2011-2020 (likely due to wider news coverage).Hispanics saw a steady rise in incidence over time, whereas Arabs were targeted more following Sept.11/2001.Crimes against Jews were largely invariant across the 30 years.Implications of these data to the wider social arena are discussed, as are directions for future research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".