Bibliographic record
Abstract
The number of people diagnosed with autism has been on the rise since the 1990s (CDC Press Release, 2020; Taylor et al., 2012; Roux et al., 2013) and roughly half of them are unemployed which results in financial cost for communities and psychological hardship for themselves (Cimera & Cowan, 2009; Howlin et al., 2005). In this conceptual chapter, I focus on the employment barriers faced by a particular type of individuals on the autism spectrum disorders (ASD), namely, individuals with mild autism (previously referred to as Asperger’s syndrome). In particular, I examine the related personal, institutional, and social barriers existing in the job market, and elucidate, how these barriers are created and perpetuated to establish an uneven playing field for ASD-affected jobseekers relative to their neurotypical counterparts. In doing so, I draw on social identity theory (SIT; Tajfel & Turner, 1986) and Goffman’s (1963) concept of social stigma to propose a model that explains how stereotypes and stigma contribute to hiring discrimination against individuals with mild autism. Lastly, I offer recommendations on how organizations can address this problem by putting interventions in place to reintegrate overlooked minorities of this sort back into the workforce.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".