МЕЖДУНАРОДНЫЙ ОПЫТ И ПРЕДЛОЖЕНИЯ ПО СОВЕРШЕНСТВОВАНИЮ РОССИЙСКИХ СТАТИСТИЧЕСКИХ НАБЛЮДЕНИЙ ЗА ЗАНЯТОСТЬЮ ИНВАЛИДОВ
Bibliographic record
Abstract
Ratification of the Convention on the rights of persons with disabilities denoted transition to a new understanding that disability results from the interaction between persons with impairments and attitudinal and environmental barriers that hinders their full and effective participation in society on an equal basis with others. The objective of this research is to suggest improvements to Russian disability employment statistics in compliance with modern model of disability on the basis of the international experience. Based on the lessons learned from the experience of countries of the European Union, Australia, Canada the authors formulated recommendations on designating groups of people with disabilities and recognizing barriers in the labor market that are be applicable to the existing Rosstat surveys and observations. Firstly, to avoid measurement errors in the number of persons officially recognized as disabled, it is recommended to use a separate question about the degree of disability. Secondly, it is proposed to use questions about longstanding health conditions and basic activities limitations. Thirdly, an additional module to Labor force survey may be included in accordance with experience of the EU countries. This module is aimed at identifying barriers in the labor market for the disabled people. It is reasonable to include in it questions on the work limitations due to health conditions and basic activities limitations, requirements in special assistance, employer awareness of the disability status, health conditions and basic activities limitations of the individual; other reasons of work limitations (not including health reasons). The questions on the barriers in the labor market have never been considered before in the Russian statistics.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.016 |
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; both teacher heads agree on what is shown here.
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".