Collective identity, social adversity and college student sympathy for violent radicalization
Bibliographic record
Abstract
Identity issues have been at the forefront in studies on determinants of youth violent radicalization. Identity uncertainty and identity fusion appear to be associated with quests for meaning, which may find some answers in extremist discourses and radical engagements. This process has been considered to be particularly important for second-generation migrants who have to negotiate multiple identities, sometimes in situations of social adversity. This paper aims to understand the relations between collective identity, social adversity (discrimination and exposure to violence), and sympathy for violent radicalization in College students in Quebec. This mixed-method study consisted of a large online survey conducted at eight colleges in Quebec. Multilevel analysis accounted for the clustered nature of data while generalized additive mixed models were used to study nonlinear relations. Results highlight the complex associations between collective identity and youth sympathy for violent radicalization. They confirm that negative public representations of minority communities may lead to more sympathy for violent radicalization. Although results suggest that strong enough identities can act as protective anchorages for youth, they also indicate that when collective identity becomes too central in personal identity this may accentuate othering processes and legitimize violence toward the out-group. These results have implications for prevention programs. They indicate that improving the public image of minority communities through mainstream media or the social media may increase youth public self-esteem and decrease their sympathy for violent radicalization. They also invite the education field to foster the development of strong plural identities.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".