Beyond “inclusion”: What bodies and identities are valued in the elementary school classroom?
Bibliographic record
Abstract
The imperative to address incidents of “bullying”, discrimination, harassment, and to introduce the inclusion of LGBTTQ+ (Lesbian, Gay, Trans, Two-Spirited, Queer) and gender diversity in elementary education is evident in a number of educational contexts. With a keen desire to move towards equality and diversity , many schools have implemented “anti-bullying”, “zero-tolerance”, and “safe school” policies and discourses in an attempt to improve the school culture and climate for LGBTTQ+/gender diverse students. Unfortunately, these attempts often do little to address the underlying binarist gender and heterosexualized regimes in elementary education that validates and privileges a particular understanding of oppressive, dominant masculinity and femininity, which are further entrenched and supported by neoliberalism and white hetero-patriarchal supremacy. How might we begin to create “queered” classroom environments and learning spaces that do not rely upon homonormative platitudes that reiterate the “queers: they’re just like us” narrative? How do we create and support queered classrooms to embody and reflect more than just simple inclusion and representation? This paper explores these questions through a queer/trans necropolitical theoretical perspective to analyze and reimagine non-normative gender and sexuality in elementary education.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".