Abstracts, Poster Presentation, Qualitative Health Research Conference, 2018
Bibliographic record
Abstract
Although there is no genetic link between race and neurodevelopmental conditions such as autism, there is an unevenness in the prevalence and outcomes for racialized children.My research aims are to ( 1) explore how such discrepancies emerge and (2) gain an in-depth account of racialized autistic children.Methodologically, I engage in a critical-affirmative cartography.This approach is informed by critical disability, childhood, and race/decolonial scholarship to map how sociopolitical structures individualize, stabilize, and hierarchize bodies and practice (e.g., normal/abnormal, abled/disabled, majority/minority, adult/ child).I also draw on process ontologies that frame bodies as dynamic sociomaterial assemblages to map how bodies affirm ways of being that lie outside normative frameworks.For this paper, I will outline my preliminary critical-affirmative cartography in which I map how cognitive, bodily and racial differences come to matter to autism diagnosis for racialized autistic children, as well as how they express capacities that often go unor misrecognized.It is not my intention to question whether racialized children are accurately diagnosed, but to consider how ascribed and diagnostic differences can interact, come to matter, and be exceeded in normative clinical and educational contexts.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.142 | 0.014 |
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".