Bibliographic record
Abstract
Abstract Reported hate crimes in the United States have increased rapidly in recent years, alongside an increase in general racial animus. Scholars have shown that the larger sociopolitical environment can directly impact the campus climate and experiences of all students, particularly students of color. However, little is known about how reports of hate crime incidents relate to college enrollment levels of students of color. This lack of evidence has especially troubling implications for Black people, the most frequent targets of reported hate crimes. This paper helps to fill in that gap by exploring the association between the number of reports of hate crimes within states and Black students’ college enrollment. We examine a comprehensive dataset of institutional enrollment and characteristics, reported hate crimes, and census data on state racial demographics from 2000 to 2017 using several techniques, including institution fixed effects. We find that a 1 standard deviation increase in reports of state-level hate crimes predicts a 17 to 22 percent increase in Black first-time student enrollment at historically Black colleges and universities. As the number of reported hate crimes is almost assuredly an undercount of the actual number of incidents, we explore the implications of what these results mean.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".