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Record W2342471578

Exploring the Social Milieu of Disability: Themes of Poverty, Education, and Labour Participation

2015· article· en· W2342471578 on OpenAlexaff
Margaret Winzer, Kas Mazurek

Bibliographic record

VenueCeON Repository (Centre for Evaluation in Education and Science) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPovertySocial exclusionCulture of povertyMedical model of disabilityEconomic growthDevelopment economicsArgument (complex analysis)InequalityScope (computer science)Life chancesChronic povertyPolitical scienceEconomicsBasic needsPsychologyMedicineSocial classPoverty reduction
DOInot available

Abstract

fetched live from OpenAlex

Although many specific aspects of the scope and functioning of disability
\nremain insufficiently explored, it is clear that disability is systematically related to
\npoverty in countries across the economic spectrum. Poverty among persons with
\ndisabilities is particularly acute in developing nations; it affects exclusion from schooling
\nand, ultimately, access to the labor market. This paper takes a multi-layered approach
\nto overview the synthesis of disability and poverty, restricted access to education,
\nand constraints to economic participation. It finds that persons with disabilities face
\ninequalities in all areas of life, throughout the life cycle, and that these inequalities lead
\nto exclusion and discrimination and to situations of poverty. The underlying argument
\nholds that disability combined with poverty creates dramatic negative impacts on the
\nsocial and economic health of individuals. Educating students with disabilities is a
\ngood investment and international agencies and national governments must increase
\nefforts to target such persons in education, development programs, and poverty
\nalleviation efforts.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.152
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.272
GPT teacher head0.464
Teacher spread0.192 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCeON Repository (Centre for Evaluation in Education and Science)Same topicRetirement, Disability, and EmploymentFrench-language works237,207