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Record W4387261324 · doi:10.3109/13668250.2023.2249276

The relationship of productivity-based wages to human rights and occupational justice – an exploratory study

2023· article· en· W4387261324 on OpenAlexaff
Rosemary Lysaght, Nicole Bobbette

Bibliographic record

VenueJournal of Intellectual & Developmental Disability · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsQueen's University
FundersAssociation of Commonwealth Universities
KeywordsIntellectual disabilityContext (archaeology)Economic JusticePublic relationsEquity (law)StakeholderProductivityPsychologyBusinessPolitical scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Productivity-based wage systems are intended to enhance the labour market participation of people with disabilities. Limited scholarship exists regarding the impact of such policies in practice. This qualitative study explored stakeholder perspectives on the Australian Supported Wage System (SWS), including perceptions of fairness and equity. METHODS: Document review provided context and background for the study. 14 semi-structured interviews were subsequently conducted with a range of stakeholders with knowledge of the SWS. RESULTS: Four primary themes were identified, related to assessment processes, value contributions of the system, practice risks and challenges, and ethical tensions. CONCLUSIONS: The SWS appears to enhance worker choice and inclusion. A variety of factors may reduce the quality of these outcomes, however, and employment systems should support a range of evidence-informed approaches to ensure equitable employment outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.009
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.136
GPT teacher head0.411
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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