Approaches to Defining the “Hate Element” of a Behavior: A Data-Driven Typology
Why this work is in the frame
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Bibliographic record
Abstract
This article addresses the proliferation of definitions and approaches used to characterize the hate element in behaviors motivated by hate, including hate crimes, hate speech, and behaviors motivated by prejudice against specific identities (e.g., homophobia, anti-Semitism, Islamophobia), and investigates whether these definitions cluster into distinct types. Using machine learning, we clustered 423 definitions from academic and gray literature in five languages between 1990 and 2021, based on 16 theoretically derived categories. The resulting typology captures the diversity of definitions from ten countries in North America, Europe, and Oceania, providing a comprehensive framework for understanding how the hate element is conceptualized in these contexts. The findings offer a basis for future research and may help inform policy responses to hate-motivated behaviors.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it