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Record W4394018376 · doi:10.1108/edi-10-2023-0366

Cultural variation in hiring people with disabilities: a theory and preliminary test

2024· article· en· W4394018376 on OpenAlexaffabout
David C. Thomas, Aminat Muibi, Anna Hsu, Bjørn Z. Ekelund, Mathea Wasvik, Cordula Barzantny

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVariation (astronomy)Test (biology)PsychologySocial psychology

Abstract

fetched live from OpenAlex

Purpose The goal of this study is to propose and test a model of the effect of the socio-cultural context on the disability inclusion climate of organizations. The model has implications of hiring people with disabilities. Design/methodology/approach To test the model, we conducted a cross-sectional study across four countries with very different socio-cultural contexts. Data were gathered from 266 managers with hiring responsibilities in Canada, China, Norway and France. Participants responded to an online survey that measured the effect of societal based variables on the disability inclusion climate of organizations. Findings Results indicated support for the theoretical model, which proposed that the socio-cultural context influenced the disability inclusion climate of organizations through two distinct but related paths; manager’s value orientations and their perception of the legitimacy of legislation regarding people with disabilities. Originality/value The vast majority of research regarding employment of people with disabilities has focused on supply side factors that involve characteristics of the people with disabilities. In contrast, this research focuses on the less researched demand side issue of the socio-cultural context. In addition, it responds to the “limited systematic research examining and comparing how country-related factors shape the treatment of persons with disability” (Beatty et al., 2019, p. 122).

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)0.002
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 score0.127
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.003
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 head0.026
GPT teacher head0.262
Teacher spread0.235 · 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 designObservational
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 routes2
Has abstractyes

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