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Shifting Landscapes: Demographic Trends Among Workers With Disabilities

2025· book-chapter· en· W4411577406 on OpenAlexaff
Nanette Goodman, Arzana Myderrizi, Giuseppe Franco Pagano

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeographyDemographic economicsEconomics

Abstract

fetched live from OpenAlex

Abstract The future of work for people with disabilities will be shaped, in part, by the demographic characteristics of workers. By analyzing data from the American Community Survey for each year between 2008 and 2022, we find that the workforce of people with disabilities is increasingly comprised of the “ADA generation,” is more ethnically diverse, and includes a higher percentage of people with post–high school degrees than the disability workforce 15 years ago. These changing demographic characteristics influence the nature of jobs for which individuals with disabilities qualify for and shape the organizational cultures and accommodations they demand. The findings underscore the need for a nuanced approach to inclusion, one that acknowledges the interconnectedness of disability, age, education, race, and ethnicity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.887
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.296
Teacher spread0.269 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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