‘New Brunswick or queer’: building queer joy through participatory collage-making with 2SLGBTQIA + youth in New Brunswick, Canada
Bibliographic record
Abstract
Amid ongoing debates on the rights of queer and trans youth across North America, this study explores novel support approaches within one Canadian province’s schools. Amendments to New Brunswick’s Policy 713 in 2023, the 2SLGBTQIA + school inclusion policy, requiring parental consent for youth’ chosen names and pronouns, stirred controversy across Canada. Additionally, a ban on third-party sexual health information in schools brought forth by the previous Conservative government has heightened educational tensions, and inspired antiqueer and antitrans policy making across Canada (see Moe 2023; Smith 2024). This inquiry draws from four participatory collage-making workshops held in the fall of 2023 across New Brunswick with 180 young people, from grades 9-12. Using participatory visual methods that centred creative expression and resistance, the workshops aimed to amplify queer and trans youth voices about their school experiences. Participants responded to prompts that asked them to create collages on the themes of queer and trans joy in schools, exploring current expressions and future possibilities amid educational and political challenges. Through collaging, a participatory visual research method, the study explores how youth navigate and express joy amidst growing hostilities in their educational environments.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.066 | 0.032 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".