(Dis)Embodied Disclosure in Higher Education: A Co-Constructed Narrative
Bibliographic record
Abstract
In this paper we use co-constructed autoethnographic methods to explore the tensions that animate the meaning of “disclosure” in university and college environments. Drawing insight from our embodied experiences as graduate students and university/college course instructors, our collaborative counter-narratives examine the ordinary ways that disclosure is made meaningful and material as a relationship and a form of embodied labour. Our dialogue illustrates the layered nature of disclosure—for example, self-disclosing as a disabled student in order to access academic spaces but not self-disclosing to teach as an instructor. Katie uses phenomenological disability studies to analyze disclosure at the intersection of disability and pregnancy as body-mediated moments (Draper, 2002). Nancy uses Hochschild’s (1983) notion of “emotional labour” to explore how socio-spatial processes of disclosure can be an embodied form of “extra work” (e.g., managing perceptions of stigmatized identities).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".