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Enhancing Comprehensive Sexuality Education for Students with Disabilities: Insights from Ontario's Educational Framework

2023· preprint· en· W4386127531 on OpenAlexaffabout
Adam Davies, Justin Brass, Victoria Martins Mendonca, Samantha O’Leary, Malissa Bryan, Ruth Neustifter

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsYorkville UniversityUniversity of Guelph
Fundersnot available
KeywordsContext (archaeology)Human sexualityCurriculumSexuality educationPedagogyPsychologyInclusion (mineral)Medical educationSociologySex educationMedicineGender studiesSocial psychology

Abstract

fetched live from OpenAlex

Comprehensive sexuality education (CSE) is an important framework utilized worldwide to provide students and young people with accurate, affirming, and socially conscious sexuality education. However, there is still a lack of CSE curricula in school contexts that is relevant for students with various disabilities. This article takes the Ontario, Canada context as an example of where and how CSE can improve to be more inclusive for students with disabilities. This article reviews the current context of CSE in Ontario, Canada, including its controversies while providing recommendations for meeting the needs of students with various disabilities, including psychological, intellectual, and physical disabilities. This article aims to provide recommendations that are relevant for scholars, researchers, and policymakers in various international contexts for improving CSE for students with disabilities in schooling.

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)0.004
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.249
Threshold uncertainty score0.871

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0310.016
Scholarly communication0.0090.003
Open science0.0020.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.189
GPT teacher head0.464
Teacher spread0.275 · 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
Published2023
Admission routes2
Has abstractyes

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