PROTOCOL: Mapping the scientific knowledge and approaches to defining and measuring hate crime, hate speech, and hate incidents
Bibliographic record
Abstract
The overallaim of the review is to map the definitions and measurement tools used to capture the whole spectrum of hate motivated behaviors, including hate crime, hate speech and hate incidents. This will benefit the field of hate studies by providing a baseline that can inform the building of cumulative knowledge and comparative research. The first review objective is to map definitions of hate crime, hate incidents, hate speech, and surrogate terms. Specific research questions underpinning this objective are: (a) How are hate crimes, hate speech and hate incidents defined in the academic, legal, policy, and programming literature?; (b) What are the concepts, parameters and criteria that qualify a behavior as being hate crime, hate incident or hate speech?; and (c) What are the most common concepts, parameters and criteria found across definitions? What are the differences between definitions and the elements they contain? The second review objective is to map the tools used to measure the prevalence of hate crime, hate incidents, hate speech, and surrogate terms. Specific research questions underpinning this objective are: (a) How are definitions operationalised to measure hate crimes, hate speech, and hate incidents?; and (b) How valid and reliable are these measures?
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.133 | 0.231 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.123 | 0.025 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".