The ins and outs of bodies: sex educators’ embodied insights on official/erotic discourses
Bibliographic record
Abstract
In this scholarship, I present insights from a sensory ethnographic study on novice educators’ embodied experiences of learning to teach sex education. I query how educators sense-make their role as knowledgeable sex educators in relation to the official and erotic discourses of sex education, and examine the experiential divisions between these discourses. Employing an arts-based approach to data generation and interpretation, my central argument proposes that increasing the permeability of the boundaries between official and erotic discourses in sex education can expand the ways learners’ bodies are understood in erotic, relational, and intimate contexts within pedagogy. By presenting these arguments, I aim to contribute to addressing the enduring challenges of the discourse of erotics ‘being’ in K-12 sex education and suggests ways to support educators in delivering effective pedagogical practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".