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Record W2766346156 · doi:10.1080/14681811.2017.1393407

Contesting consent in sex education

2017· article· en· W2766346156 on OpenAlexaffabout
Jen Gilbert

Bibliographic record

VenueSex Education · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsHuman sexualityArgument (complex analysis)CurriculumPassionGovernment (linguistics)Gender studiesInformed consentSociologyPolitical scienceLawPsychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

This paper explores discourses of affirmative consent in sex education curriculum and policy. It traces the ways in which discourses of consent have emerged in sex education debates, focusing first on the activism of two young women in Ontario, Canada who lobbied the provincial government to include discussions of consent in a new Health and Physical Education (HPE) curriculum. Their activism is instructive for the ways in which their lobbying was eventually subsumed into the logics of curriculum, with learning outcomes and lesson plans taming the passion of their protest. As others see in sex education the answer to sexual violence, we cannot forget that sex education is, in part, a defence against passion, and that consent – once swallowed up by HPE – might also work to tame the unruliness of sexuality. Turning to age of consent laws – another arena where discourses of consent discipline the sexuality of young people – I ask how our pedagogical and legal address to sexuality paradoxically refuses its force. This is the central argument of this article – namely, that the concept of consent brings with it, into education, a procedural logic that misrecognises sexuality as a transparent, communicative, and rational experience and mistakes compliance for learning.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.101
GPT teacher head0.489
Teacher spread0.389 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations89
Published2017
Admission routes2
Has abstractyes

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