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Record W96167856 · doi:10.25071/1705-1436.107

Disability Disclosure in the Workplace

2006· article· en· W96167856 on OpenAlexvenueno aff
Robert Wilton

Bibliographic record

VenueJust Labour · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationDismissalReasonable accommodationWork (physics)Qualitative researchPublic relationsPsychologyPosition (finance)Social psychologyBusinessSociologyPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

This paper is concerned with workplace accommodation and the extent to which people feel able to disclose disabilities at work. Disclosure is central to accommodation in the sense that workers must feel comfortable describing their needs to employers. Where this is not the case - for example, where workers are concerned about the precariousness of their position and the fact that disclosure may result in dismissal - legal requirements for accommodation can be ineffective. To explore this issue, the paper uses qualitative data from interviews with fifty-nine people with physical, learning, psychiatric and sensory disabilities in the Hamilton labour market. Analysis indicates that most respondents viewed disclosure as a risky endeavour, and a significant minority did not disclose due to concerns about not being hired or being dismissed. The conclusion discusses the need for accommodating workplaces and the implications for the labour movement.

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0000.006
Research integrity0.0010.001
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 head0.120
GPT teacher head0.403
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations32
Published2006
Admission routes1
Has abstractyes

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