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Record W7132950965

Ontario Elementary Teachers' Preparedness to Administer Comprehensive, LGBTQ-Inclusive Sex Education

2017· dissertation· W7132950965 on OpenAlexaboutno aff
Jennifer T. Rigby

Bibliographic record

VenueTSpace · 2017
Typedissertation
Language
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessCurriculumSex educationOpposition (politics)Religious educationSchool teachers
DOInot available

Abstract

fetched live from OpenAlex

In 2015, Ontario introduced an updated, LGBTQ-inclusive sexual health curriculum for all grades which inspired opposition from some conservative religious groups. This study explored the experiences and preparedness of elementary teachers covering this curriculum using a survey. Researchers predicted that: (1) teachers would feel least prepared to cover the controversial topics, (2) low levels of engagement in the reform and encountering complaints would predict lower preparedness, and (3) teachers in religious schools would encounter more obstacles than those in secular schools. The majority of participants felt comfortable, knowledgeable, effective and motivated to teach the curriculum. Participants felt least prepared to teach four of the most controversial topics. Support for LGBTQ-inclusive education, positive student reactions, and less fear of parent complaints predicted greater preparedness to teach sex education. There were few differences between religious and secular teachers. Respondents desired more training, teaching resources, support from administration and time to teach health.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.073
GPT teacher head0.504
Teacher spread0.431 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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