Approaches to Defining the “Hate Element” of a Behavior: A Data-Driven Typology
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".