Valuable Diversity or Pathological Problem?: A Comparative Thematic Analysis of Self-Advocate and Adapted Physical Activity Teachings About Autism
Bibliographic record
Abstract
This article offers a comparative thematic analysis of two datasets: the online writings of Autistic self-advocates on navigating neurotypical programs (emic dataset), and discussions on autism within the most widely circulated undergraduate Adapted Physical Activity (etic dataset). Our 3 themes describe some of the most significant ways that APA textbooks differed from the teachings of Autistic self-advocates: 1) Writing in Categorical Imperatives, 2) Problematizing and Pathologizing Autism and Autistic Characteristics, and 3) Promoting ABA and Autism Speaks. We end with a discussion of how these findings demonstrate a dissonance not only between APA and Autistic knowledges, but also between APA textbook approaches to Autism and APA’s stated core values, including dignity, choice, self-determination, and self-advocacy.
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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".