Bibliographic record
Abstract
According to a House of Commons Report (2022) on public safety, hate motivated ideologies are spreading consistently across Canada. Moreover, the Report cites youth among those particularly vulnerable to right-wing ideologies thereby complementing the literature that suggests that youth and radical right-wing ideologies are positioned at a precarious intersection. Particularly notable and worth distinguishing are youth cultural right-wing extremist groups whose ambition is not completely independent from right-wing extremists. Youth cultural right-wing extremist groups especially rely on the internet and social media as a means of recruiting members to the organization and to spread widely their radical ideologies. It is not enough, though, to make overly simplistic claims that youth, such as the youth cultural right-wing extremists, have a periphery existence in cyber spaces and a generalized association to more sophisticated right-wing extremist groups. Youth cultural right-wing extremist groups are characteristic of cultures that consist of a hateful discourse that celebrates ethnonationalist values, beliefs, and traditions. Research points to the fact that the dialogue, often antagonistic, appeals to youth that are attracted by these radical views. Education systems have been identified as sites where radical and extremist tendencies and ideologies can be directly addressed by educators, given the substantial amount of time that youth spend in schools. The paper discusses both the interventions and implications related to addressing extremist ideologies in educational contexts.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".