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Record W3093953412 · doi:10.1080/00131911.2020.1829559

Supporting transgender students in schools: beyond an individualist approach to trans inclusion in the education system

2020· article· en· W3093953412 on OpenAlexafffundabout
Wayne Martino, Jenny Kassen, Kenan Omercajic

Bibliographic record

VenueEducational Review · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInclusion (mineral)TransgenderIndividualismSociologyViewpointsExemplificationRationalityPoliticsPedagogyPublic relationsEpistemologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this article, we provide theoretically informed empirical insights into administrative and pedagogical approaches to supporting transgender students in schools which rely on a fundamental rationality of individualisation and rights. We draw on trans epistemological frameworks and political theories that address the limits of liberal individualism to provide insights into how transgender inclusion and recognition are conceived and enacted in one particular school in Ontario. Our case study contributes to an emerging body of research that documents the viewpoints of educators in response to the increasing visibility of trans youth in schools and a growing awareness of their experiences which have highlighted the institutional and systemic barriers continuing to impact on the provision of support for transgender students in the education system. Overall, the case study serves as an illustrative exemplification of the problematic of trans inclusion when it is driven by a logics of liberal individualism and rights that fail to address broader forces of cisnormativity and cisgenderism.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.108
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.016
Scholarly communication0.0080.005
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.099
GPT teacher head0.486
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2020
Admission routes3
Has abstractyes

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