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Record W4413016990 · doi:10.1080/00380253.2025.2538171

Working Part-Time: Earnings Penalties Among People with Disabilities Across Occupational Groups in France

2025· article· en· W4413016990 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSociological Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersAgence Nationale de la Recherche
KeywordsEarningsPsychologyOccupational prestigeDemographic economicsWorking timeSociologyLabour economicsEconomicsDemographySocioeconomic statusWork (physics)AccountingEngineering

Abstract

fetched live from OpenAlex

Although part-time work can provide important accommodations and flexibility for people with disabilities, the rise of contingent labor has left many of these workers in lower-paid jobs that are often associated with manual labor or routine service work. Why do people with disabilities work part-time? What are the benefits and drawbacks of part-time work, and do these vary across occupations? Using data from the 2013–2019 French Labor Force Survey (N = 122,033), we find that people with disabilities are more likely to work part-time across occupations, citing health reasons or the lack of full-time work opportunities. These higher rates of part-time work contribute to disability-related earnings gaps, but even among part-time workers, people with disabilities earn less than those without disabilities and experience wage penalties that can vary across occupations. By focusing on the reasons for part-time work and its effects across occupational groups, our analysis highlights structural factors—beyond disability status—that shape employment outcomes and are not fully addressed by equal pay policies for part- and full-time workers.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.768

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 studies0.0010.002
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 head0.096
GPT teacher head0.392
Teacher spread0.296 · 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