The Enlightenment of social model of disability on the formulation of employment policy for the disabled----Take China's disabled employment policy as an example to analyse
Bibliographic record
Abstract
This article introduced China’s leading employment policy for the disabled at first. And then, based on the analysis of the difference between the medical and social model of disability, the transformation from medical to social model of disability in legislation in US and UK were summarized, and the advantages of the social model of disability were admitted in guiding the employment legislation for the disabled. Furthermore, from the perspective of the social model of disability, this article summarized the problems behind the employment policy for the disabled in China and the according enlightenments were put forward. Firstly, China’s government should consider integrating social model in policy-making on the basis of its own situation. To be specific, the formulation of policies should not only focus on the injured individuals, but also pay more attention to the structural barriers in the society that the disabled have suffered. The new objective of those policies should aim at eliminating social oppression and discrimination that widely exist in the modern world. Secondly, China should further optimize the policies without the regulations for the reward or the punishment, so as to enhance the prestige of the policies. Finally, the enforcement mechanism should also be improved to a large extent, therefore truly contributing to the implement of employment policy for the disabled.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".