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Record W4393097268 · doi:10.1386/jaah_00161_1

‘I take them on as if facts in a book’: Sex educators’ cumulative witnessing of sexual trauma

2024· article· en· W4393097268 on OpenAlexaff
Kathleen A. Hare

Bibliographic record

VenueJournal of Applied Arts and Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsPsychologyPsychoanalysisDevelopmental psychology

Abstract

fetched live from OpenAlex

Through a visual essay, the author explores the intersection of trauma and sexual health education (SHE) using art-based expressions from an ethnographic exploration of novice educators’ embodied experiences of SHE training. In particular, the author examines educators’ engagements with two forms of trauma: (1) self-trauma: trauma personally experienced by sex educators and (2) trauma transposition: other individuals’ disclosures of past harms to the educators. The author theorizes these engagements together as a form of cumulative witnessing – the collective, excessive consumption of violence through direct and vicarious exposures. Inspired by palimpsest methods, carbon tracing paper and photography were used to express the educators’ cumulative witnessing of sexual traumas via visually layering their words, drawings and expressed feelings about the sexual traumas that thread through SHE. The inquiry highlights key implications for SHE pedagogical practices, including acknowledging trauma, dealing with trauma disclosures and learning from and with trauma.

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)0.011
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 score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.014
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.110
GPT teacher head0.395
Teacher spread0.285 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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