Bibliographic record
Abstract
In this research, I take an autoethnographic approach to center my own Mad disabled voice. I reflect on personal challenges and understandings as I was introduced to medical sociology as an undergraduate and went on to pursue graduate studies in critical disability studies. Although my first attempt at a bachelor’s degree was cut short due to disability 20 years ago, I had never thought much about what it was to be disabled or how it would affect the completion of my degrees. However, my return to college was marked with the need to assure others my disabilities had been acceptably resolved to even have the opportunity to learn. And disability—my own, my family’s, others—has remained an impactful dynamic throughout my educational journey. Through an exploration of medical sociology, outdated models of disability, disability studies, and disability theories—I reflect on the personal, educational, research, and pedagogical implications these have had for myself, as a Mad disabled critical disabilities scholar. As I was introduced to each of these, I found my understandings of self, those I cared for, and interactions with medical professionals changing. When I found myself traversing new and increasing disabilities, my personal and academic journeys collided, as I advocated to establish myself as able with limits. I consider the educational and historical relevance of medical sociology and outdated models of disability for critical disability scholars, presenting a Mad disabled perspective on studying sociology and critical disability studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".