How can Queering Contribute to Elementary Schoolteachers’ Understanding and Classroom \nPractice, as They Design and Implement LGBTQ Sensitive Visual Arts Curriculum?
Bibliographic record
Abstract
As a Design Based Research (DBR) qualitative study, this thesis is positioned at the intersection of the Quebec Visual Arts Education Program and lesbian, gay, bisexual, trans and queer (LGBTQ) youth studies. Specifically, it examined the creation process, precisely the design and implementation, of six elementary school teachers’ LGBTQ sensitive Visual Arts Curriculum and their learning, understanding, and practice of Queering. The six teachers work at two elementary schools in Montreal, Quebec: one in Notre-Dame-de-Grace and one in the Plateau Montreal, \nincluding three teachers per school, one per cycle. The research illuminates the issues around their Queering of Elementary Visual Arts pedagogy, through the development and implementation of lessons that were: inclusive of various family constructs, confronting gender \nstereotypes, and challenging the ideas around bullying. The study employed DBR combining qualitative data collection (interviews and logs). Keeping in mind queer as strategy, an attitude, and a new understanding (Smith,1996), while celebrating difference and breaking heteronormative binaries, was at the heart of the teacher’s design approach as they created the curriculum. This lead to the creation of a series of lesson plans and a guide of Best Practices to be used when implementing such lesson plans in the Elementary classroom.
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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 | 0.013 | 0.019 |
| 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 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".