Bibliographic record
Abstract
In light of Premier Doug Ford’s 2019 reconstruction of Ontario’s sex-ed curriculum, which has scrapped/limited conversations regarding masturbation, consent, gender identity and sexual orientation in elementary classrooms, conversations around 2SLGBTQIA+ youth’s safety and visibility in Ontario schools are poignant and necessary. Ford’s changes calmed the moral panic of Ontario parents sparked by Kathleen Wynne’s 2015 curriculum, which prioritized the normalization of consent and 2SLGBTQIA+ identities. Many claimed that Wynne’s curriculum sexualized children and was inappropriate for students whom adults have positioned as straight and homophobic in the classroom (Bruhm & Hurley, 2004). Most of the backlash Wynne faced—and most of the support Ford received— came from parents and educators in Ontario’s Catholic schools who claimed Wynne’s curriculum went against their constitutional right to teach the values of their faith (Pierre, 2019). When internalized shame and outward hate combine in the Catholic school system, which already lacks diversity and empathy, the classroom can become a scary place for queer children. I argue that incorporating queer stories into classroom libraries and supporting student-led Gay-Straight Alliances (GSA) can provide queer children with a safe space in an institution that otherwise works to deny and mystify their existence. Children’s books like Jessica Herthel and Jazz Jennings’ I Am Jazz, and Miriam B. Schiffer’s Stella Brings the Family can work to foster empathy, deconstruct harmful heteronormative structures and normalize queerness in the classroom to make room for Queer joy and viability in Ontario schools.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".