MétaCan
Menu
Back to cohort
Record W1918467556 · doi:10.5539/mas.v9n12p77

Social Integration of Disabled People in Russia Using Virtual Computer Technologies

2015· article· en· W1918467556 on OpenAlexvenueno aff
Galina I. Efremova, Zhanna A. Maksimenko, Rimma M. Aysina, Inna Vladimirovna Petrova

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDisabled peopleLife spanPsychologyRentingLiteracySocial integrationQuality of life (healthcare)Quality (philosophy)Process (computing)GerontologyComputer scienceSociologyApplied psychologyPolitical sciencePedagogyMedicine

Abstract

fetched live from OpenAlex

The article is devoted to the topical problem of persons with disabilities integration into society. Intensions and realities of social policy for disabled people in Russia are introduced. social and psychological barriers, obstructive to this process are described. The results of the survey in a sample of 268 disabled individuals are presented. It is concluded that the low level of community literacy, the lack of communication skills, social stereotypes concerning disabled people and “rental income attitude” of persons with disabilities are the most severe social and psychological barriers. Virtual computer technologies are offered as one of the modern tools, aimed at overcoming the barriers. Global and domestic experience of using virtual technologies for rehabilitation of disabled people are discussed. Technology of improving the life quality of persons with disabilities by means of virtualistics is proposed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.085
GPT teacher head0.323
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicEducational Innovations and ChallengesFrench-language works237,207