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Record W3122272540 · doi:10.1080/09687599.2020.1867073

Disability and inclusion in Kazakhstan

2021· article· en· W3122272540 on OpenAlexaboutno aff
Julie Allan, Tolkyn Omarova

Bibliographic record

VenueDisability & Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
FundersMinistry of Education and Science of the Republic of Kazakhstan
KeywordsInclusion (mineral)Political sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

The Republic of Kazakhstan ratified the United Nations Convention on the Rights of Disabled People in January 2015 as part of a programme of ‘Future without Barriers’ and has sought to make the majority of its schools inclusive. This paper reports on progress towards inclusion and the challenges faced by a nation that is aspiring to develop as an independent nation whilst still utilising knowledge and capital from the former Soviet Union. An analysis of the country’s ‘readiness’ for inclusive education is offered, using Hacking’s 2010 Hacking, I. 2010. “Autism Fiction? A Mirror of an Internet Decade?” University of Toronto Quarterly 79 (2): 632–655. doi:10.3138/utq.79.2.632.[Crossref], [Web of Science ®] , [Google Scholar] concept of (2006 Hacking, I. 2006. Kinds of People: Moving targets. British Academy Lecture, 11 April. Retrieved on 30 May 2018 from: https://www.britac.ac.uk/sites/default/files/hacking-draft.pdf. [Google Scholar]; 1998a Hacking, I. 1998a. “Making up People.” In Science Studies Reader edited by M Biagioli. London: Routledge. [Google Scholar] & b Hacking, I. 1998b. Mad Travellers: Reflections on the Reality of Transient Mental Illnesses. Charlottesville, VA: University Press of Virginia. [Google Scholar]; 2010 Hacking, I. 2010. “Autism Fiction? A Mirror of an Internet Decade?” University of Toronto Quarterly 79 (2): 632–655. doi:10.3138/utq.79.2.632.[Crossref], [Web of Science ®] , [Google Scholar]) ‘making up people’ and Mitchell’s ‘ablenationalism’. Ablenationalism is also used to explore how Kazakhstan’s efforts to assert its own distinctive identity and culture affected the positioning of disabled children and adults within education and within society. We conclude with some considerations of the possibilities for the future rights of disabled children and for inclusion. Points of interestThis article focuses on the republic of Kazakhstan, which separated from the Soviet Union in 1991.The country has made significant efforts to introduce inclusive education over a short period of time and there has been some success. A major barrier to progress comes from the country’s system of assessing and classifying disabled children.Teachers have found inclusive education challenging and many have expressed a preference for segregation.Parents faced enormous challenges in having their disabled children accepted in mainstream schools. However, by establishing parents’ groups, they have been able to influence policy and practice and increase progress towards inclusive education.Recommendations made to the Ministry of Education and Science of Kazakhstan, who commissioned this research, included adopting an international perspective to enable it to learn from other countries; incentives to schools to encourage greater inclusiveness and extended media campaigns to change attitudes towards disability.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0170.011
Scholarly communication0.0080.003
Open science0.0010.013
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.344
Teacher spread0.321 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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