Bibliographic record
Abstract
While the typical hate crime is perceived to be of a violent nature perpetrated by individuals connected to Nazi and neo-Nazi groups and white supremacists, a more insidious form of hatred exists in the form of hate propaganda (Kinsella 1994; Martin 1995; Sher 1983; Sunahara 1981; Abella and Trooper 1982; Barrett 1987; Betcherman 1975; Bolaria and Li 1985; Frideres 1976). In Canada “hate messages take a variety of forms including flaming crosses, heckling at memorial services, music, and desecration of synagogues, mosques, or temples” (Commission for Racial Equality 1999, quoted in Kazarian 1998, 204). “In the winter of 1992, alone protestor at an Ontario university disrupted a Kristallnacht (night of broken glass) ceremony in memory of the 1938 attack by Nazi soldiers on Jewish homes and businesses” (Gillis 1993, quoted in Kazarian 1998, 204).
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".