Disrupting "bully" talk : progressive practices and transformative spaces for anti-violence work in schools
Bibliographic record
Abstract
Bullying in schools is the target of much attention from scholars, the media, educators, policy makers and families. At the same time, there is sparse discussion about how bullying is constructed and little critique about the hegemonic assumptions that shape popular notions of bullying. The primary goal of this study was to challenge discourses that relegate violence in schools to the realm of the private, thus obscuring the role of structural inequalities. Through the lenses of feminist post-structuralism and post-colonialism, data were collected and analyzed including individual and focus group interviews with 15 social justice educators who work with high-school students—seven community-based educators and eight school-based educators. Other data include scholarly literature, media stories, and British Columbia Ministry of Education resources. Utilizing critical discourse analysis, I traced four overarching discourses about bullying: the first, a discourse of deficit and deviance, pathologizes young people by focusing on a problem set of behavioural traits; second, a discourse of production and reproduction turns a critical gaze to the family as a site for producing bullies and victims; third, a discourse of amalgamation and conglomeration constructs bullying as an all encompassing explanation for violence; and, in the fourth discourse—a discourse of tokenism and tolerance—care and respect are given only superficial and depoliticized consideration. At the same time, emerging from the interviews with the participants, I identified a marginal, more hopeful, oppositional discourse that is built from a critique of difference and dominant masculinity and centres on critical notions of citizenship, community, and safety. With an aim of nurturing counterpublics in which violence with young people can be problematized and disentangled, strategies of resistance—such as rewriting relationships with young people, cultivating connections and coalitions, and working organically—are highlighted. It is clear that bullying, as currently constructed, constrains young people and those who act with and for them in particular ways. I conclude the dissertation with reflections on the implications for both educational policy and pedagogical practice of a re-worked approach to school violence, one the foregrounds inequality, difference and exclusion and aims to promote social critique and positive social change.
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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 | 0.001 |
| 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".