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Record W4396555454 · doi:10.15173/cjae.v4i1.5422

Autistic Perspective on Workplace Disclosure and Accommodation

2024· article· en· W4396555454 on OpenAlexaff
Eric Samtleben

Bibliographic record

VenueCanadian Journal of Autism Equity · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsTrent University
Fundersnot available
KeywordsAccommodationPerspective (graphical)Reasonable accommodationPsychologyBusinessComputer sciencePolitical scienceLawNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

The negative stereotypes associated with autism have created many barriers to employment. As a result, the autistic population has some of the lowest workforce participation rates among all types of disability; with about only one-quarter of the working age population actively participating. These low unemployment rates persist despite many autistic people expressing the desire to work and being more than capable to do so. Among successfully employed autistic people, disclosure and effective accommodations appear to be key factors for the maintenance of long-term employment. Thus, the present study aimed to provide a qualitative exploration of autistic perspectives on how managers/organizations can encourage disclosure and accommodation requests. In addition, this project sought to explore how managers and/or organizations can best support their autistic employees following an accommodation request. Results from the thematic analysis revealed four primary themes (i.e., authentic culture of caring and inclusivity; strengths approach; individualization and collaboration; and clear and consistent structure/communication) stratified by two categories (i.e., encouraging disclosure and accommodation requests; and supporting autistic employees). The results from this study provide managers/organizations with a practical framework for encouraging disclosure and informing the accommodation process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.351
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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