Out of the Closet and into Quarantine: Stories of Isolation and Teaching
Bibliographic record
Abstract
Being queer can be filled with moments of isolation: not fitting in to heteronormative rites of passage, not knowing if or when to come out in academia, and now, trying to cope with the difficulties induced by officially-mandated social distancing in a global pandemic. Although isolation is a common human experience, for queer people it is often an intimate part of their stories, leaving lasting scars. Experiences of isolation, loneliness, and being “othered” have serious consequences. Through autoethnographic queer inquiry, we explore isolation and how it shapes teaching and learning. Drawing on concepts of the outsider-within and the uncanny, and distinguishing isolation from loneliness and solitude, we share our personal stories of isolation through the perspective of a performative “I”, examining how our pedagogical philosophies and practices inevitably reflect our queer experiences. Coming from different disciplines of practice, we met because of the COVID-19 pandemic, which prompted this return to old and new forms of social isolation—the old being the experience of growing up queer and the new through teaching online. From our perspectives across a generational divide, we trace the unsettling experiences of being queer and teaching in our COVID bubbles, and we attempt to navigate ourselves and our students safely through disconnection and isolation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.063 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".