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Record W2805237620 · doi:10.17501/wdrc.2017.2105

EMPOWERING PERSONS LIVING WITH DISABILITIES VIA INCLUSIVE PRACTICES IN ORGANIZED SECTORS - LEARNINGS FROM DEVELOPED NATIONS

2018· article· en· W2805237620 on OpenAlexaboutno aff
Herratdeep Singh, Prerana Pandia, R.baskran A l Raman Nair

Bibliographic record

VenueWorld disability & rehabilitation conference · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsUniversal designEconomic growthPolitical scienceComputer scienceWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

Issue of disability has figured on International Human Rights agenda in on several occasions. The existence of disability often results in decrease in social and economic well-being. Understanding the numbers of people with disabilities and their circumstances can improve efforts to remove disabling barriers and provide services to allow people with disabilities to participate. Present research has analyzed the practices in developed nations under organized sectors to how they empower the people living with disabilities to get their rightful space in the areas of work and education. Present research has systematically reviewed research papers, reports (years 2001to 2017), case studies, epidemiological studies via online database 'PubMed', 'Google Scholar' and 'Web of Science'. The good practices, lessons, barriers have been identified to advocate such good practices in developing nations. There is lot to learn from countries like Switzerland, Norway, Canada etc., which have the highest rates of employment for persons with disabilities. The participation of people with disabilities is important for promoting human dignity, social cohesion and accommodating the disabled working age population. There are ample of good practices in developed nations which we can adopt to make the work environment most inclusive, equitable and enabling for people living with disabilities.

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)0.001
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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