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Record W3010640223

Mapping SOGI Inclusion through Curriculum and Practice in a Canadian Teacher Education Program

2019· article· en· W3010640223 on OpenAlexvenueaboutno aff
Kedrick James

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumInclusion (mineral)SyllabusSexual orientationIdentity (music)PedagogyPsychologyConversationSexual identitySociologyMedical educationMathematics educationMedicineGender studiesSocial psychologyHuman sexualityArt
DOInot available

Abstract

fetched live from OpenAlex

Through a faculty-wide program enhancement campaign implemented in a British Columbia university, we investigated sexual orientation and gender identity (SOGI) awareness and inclusion in a Canadian teacher education program. Comparing data from curriculum mapping of course outlines, close analysis of a departmental cross-section of 49 undergraduate syllabi, exit survey data, and 20 interviews with faculty and staff involved in the program, we observed how sustained conversation at all levels of program delivery is indispensable. Curriculum hours of formal SOGI-specific instruction were limited, yet most teacher candidates self-reported that they felt sufficiently prepared to support non-heteronormative students. Findings indicated that SOGI inclusion relies less on formal curriculum than the responsiveness of educators—under sway of progressive policy changes—to have informal, identity-inclusive conversations and to forge connections to curriculum content. Keywords: teacher education, sexual orientation and gender identity, inclusive education, curriculum policy, classroom discourse

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0140.005
Scholarly communication0.0040.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.334
Teacher spread0.299 · 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 designQualitative
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

Citations2
Published2019
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Education / Revue canadienne de l éducationSame topicGender Roles and Identity StudiesFrench-language works237,207