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Record W4224884295 · doi:10.7565/ssp.v5.6738

Understanding Sexual Consent Among Adolescents

2022· article· en· W4224884295 on OpenAlexaff
Carolyn O’Connor, Stephanie Begun

Bibliographic record

VenueSocial Science Protocols · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsycINFOThematic analysisPsychologyInformed consentPopulationQualitative researchSexual violenceMedical educationClinical psychologyMedicineMEDLINECriminologyPolitical scienceSociologyAlternative medicineSocial science

Abstract

fetched live from OpenAlex

Background: Sexual consent remains one of the most important tools in the prevention of sexual violence, for which adolescents are an especially vulnerable group. However, it is unclear how sexual consent processes are defined and used by this population. To bridge this gap in knowledge, we present a protocol for a forthcoming scoping review that will identify and synthesize the available empirical research findings on sexual consent conceptualizations and processes among adolescents. Methods/Design: Using the framework by Arksey and O’Malley (2005), a systematic search of six academic databases (Education Source, ERIC, Gender Studies Database, PsycINFO, Social Services Abstracts, and Sociological Abstracts) will be conducted; this range has been selected due to the multi-disciplinary nature of sexual consent research. Following two levels of screening, data from the full-text articles will be charted and subjected to qualitative thematic analysis. Discussion: These collated results will provide a map of key concepts and establish gaps in the extant literature in order to guide future research on this topic. The findings will advance our knowledge of sexual consent as it is understood by the adolescent population; they may also inform the content and delivery of sexual education programs to ensure that they are relevant to their target audience and assist in the prevention of sexual violence.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0130.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.286
GPT teacher head0.443
Teacher spread0.157 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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