EMPOWERING PERSONS LIVING WITH DISABILITIES VIA INCLUSIVE PRACTICES IN ORGANIZED SECTORS - LEARNINGS FROM DEVELOPED NATIONS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".