Peer Aggression, Canadian Style: Multiculturalism and Xenobullying
Bibliographic record
Abstract
In Canada, many first- and second-generation minority immigrants experience oppression based on their cultural characteristics. The dominant culture segregates and ostracizes the minority youths, which has been rendered invisible through democratic racism. The minority immigrant youths experience daily aggression within the school setting, perpetrated by students from their ethnic community, students from other minority ethnic communities, white students, teachers, counsellors, and school personnel. This dissertation problematizes Canadian democratic racism, which facilitates the label of an outsider for the minority immigrant youths. While some of these young adults are born in Canada, they do not believe they belonged to this nation. Democratic racism is what silences these young citizens. \n \nThe immigrant youths' experience with aggression is not just within an educational institution. They also learn to manage aggression outside of the school system and at a societal level. After migration, these young adults become their immigrant parents' communicators and translators. These positions burdened with the labour of love heavily strain their social experience with the dominant culture. The extreme pressure that the minority immigrant youths endure from their parents and the lack of acceptability these developing young adults receive from the dominant culture create a sense of unhomeliness for them. These young people do not perceive themselves as members of their own ethnic communities, yet they do not feel they belong to their new host country. In my dissertation, I study aggression against minority immigrant youths in micro, mezzo, and macro levels through qualitative interviews. I question how the directed aggression and micro-aggression would affect the minority youths' identity formation and sense of belonging to their host country. \n \nIn addition to exploring immigrant youths' physical interactions and face-to-face aggression, I examine the effects of cyber technology on youths' lives in general and minority immigrant youths in specific. I argue that while minority immigrant youths still prefer face-to-face interaction to torment each other physically, these young people utilize the cyberworld for other online harassment, such as sexual harassment and stalking.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".