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Record W3199279345 · doi:10.18061/dsq.v41i3.8396

Expanding the meaning of citizenship: "evacuation" of people with disabilities in Russia from the institutions during COVID-19

2021· article· en· W3199279345 on OpenAlexaff
Alfiya Battalova

Bibliographic record

VenueDisability Studies Quarterly · 2021
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizenshipCoronavirus disease 2019 (COVID-19)Public relationsLegislaturePolitical scienceContext (archaeology)ExpeditingBusinessPublic administrationSociologyPoliticsMedicineEconomicsLawManagement

Abstract

fetched live from OpenAlex

In the wake of COVID-19, non-profit organizations that focus on providing social support services in Russia consolidated their efforts to take proactive measures to change internaty 1, the system of institutions for people with physical, mental, and intellectual disabilities. As a result of advocacy efforts by the non-profit organizations, 26 people from these institutions were evacuated and provided with temporary assisted housing. The decision to act proactively to prevent the spread of the virus among the residents of the institutions is indicative of the galvanized efforts of the non-profit sector to advocate for deinstitutionalization and assisted living. COVID-19 served as an opportunity for the non-profit organizations to emphasize the need for expediting deinstitutionalization reform. Drawing on media sources, the literature on disability and advocacy in Russia, and the conceptual framework of citizenship, this paper will provide an overview of the internaty system, analyze the legislative context of disability and COVID-19, and discuss the context of deinstitutionalization advocacy in Russia.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.016
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.392
Teacher spread0.286 · 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 designQualitative
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

Citations4
Published2021
Admission routes1
Has abstractyes

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