Researching autism, becoming disabled: discovering brilliant imperfection
Bibliographic record
Abstract
Here, I take Eli Clare's concept of "brilliant imperfection" as a jumping-off point to reflect on how my PhD research into autistic experiences of intimacy led to an engagement with -and understanding of -my own relationship to disability.Life imitated research as participants' narratives held a mirror up to my own experience of growing up gay, my life with HIV, and my adult diagnosis of ADHD.Far from being a dry scholarly exercise, the PhD journey came to represent a personal, professional, and academic transformation.As a way of knowing, understanding, and living with disability and chronic illness, brilliant imperfection is rooted in the nonnegotiable value of body-mind difference.It resists the pressures of normal and abnormal.It defies the easy splitting of natural from unnatural.It has emerged from collective understandings and stubborn survivals.(Eli Clare 2017, xvii, original italics) I came 'late' to a university education: 'late' with inverted commas because crip time then was not even a dot on my intellectual horizon.I started researching autistic experiences of sexuality and intimacy -first for a master's degree and then a PhD (Jackson-Perry 2023) -as a non-autistic student.Almost a decade later I emerged, as Clare promises, through "collective understandings and stubborn survivals," as a disabled, neurodivergent researcher.This came about through three points of encounter with my research.Over the past 20 years I have had three intense interests, which I have explored academically, in my work life, and in personal reflections about the world and my place in it.These are Deafhood, which was my first conscious critical engagement with questions relating to disability; HIV and Aids, which I have lived with for over 30 years and is the area in which I work at a teaching Hospital in Switzerland, and autism and intimacy, my research subject.These
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".