Queering Seduction: Eros and the Erotic in the Construction of Gay Teacher Identity
Bibliographic record
Abstract
This article situates itself in cross-disciplinary work to combine reflection on my own teaching and my own “queer locatedness” (Grace, 2000) with more traditional elements of media studies and critical pedagogy. The circle around this reflection is defined by ruminations on the relationship between eros and the erotic, media representations of teachers, and morality and mortality in male identity and pedagogy. I offer several tentative conclusions: the role of a queer teacher is more conflicted than that of a heterosexual; good teaching may not be possible unless there is an element in it of seduction, but it is teachers who are seduced by their students. Good teaching—ethical teaching—celebrates differences by exploring them. Interrogating Eros in teaching leads to an appreciation of the sweetness of morality and the stench of mortality along the parabola connecting body and mind, and the construction of a queer teaching identity.
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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 | 0.012 | 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 | 0.000 | 0.001 |
| 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".