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Record W4380683580 · doi:10.1080/09540253.2023.2222128

Religious reactions to gender identity: a comparative analysis of select Canadian and Australian Catholic schools

2023· article· en· W4380683580 on OpenAlexafffundabout
Tonya D. Callaghan, A. Esterhuizen, Leanne Higham, Michelle Jeffries

Bibliographic record

VenueGender and Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGender studiesDiversity (politics)SociologyTransgenderDoctrineSexual orientationIdentity (music)Sexual identityPerspective (graphical)Religious educationPolitical scienceLawHuman sexualityPedagogy

Abstract

fetched live from OpenAlex

Determining the depth of discrimination against gender and sexual minority groups in Catholic schools of selected western nations is best undertaken from an international-comparative perspective. In this article, we compare the Canadian case of Alberta’s ‘washroom wars’ and a ‘gender row’ over uniform changes in an Australian Catholic high school. In each case, practises inclusive of gender diversity in Catholic schools were framed as a departure from Catholic doctrine. To explore how oppressive structures exist and operate within schools, we examine media accounts of each case using Critical Discourse Analysis and contextualize this analysis by examining Canadian and Australian educational and legal settings. We find that despite differing legal frameworks, some Catholic schools continue to place Canonical law above the rights of transgender and gender-diverse students in both countries. We therefore argue that it is the Catholic system’s institutional stance on gender and sexual diversity that perpetuates discrimination.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.070
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.011
Science and technology studies0.0220.008
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.401
Teacher spread0.317 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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