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Record W2915334022 · doi:10.1177/105678791802700404

Increasing Inclusion and Reducing the Stigma of Special Needs in Latvia: What Can We Learn from Other Countries?

2018· article· en· W2915334022 on OpenAlexaffabout
Zaiga Mikelsteins, Thomas G. Ryan

Bibliographic record

VenueInternational Journal of Educational Reform · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsNipissing University
Fundersnot available
KeywordsSocial exclusionPovertyStigma (botany)LatvianEuropean unionIndependence (probability theory)Economic growthInclusion (mineral)Political sciencePopulationSpecial needsPsychologyPublic relationsSocial psychologyMedicineBusinessEnvironmental healthEconomicsPsychiatry

Abstract

fetched live from OpenAlex

Latvia regained its independence in 1991 and has been slowly transforming the education system to meet the standards of the European Union (EU) and the Western world. Since regaining independence Latvia has started to integrate children with special education needs into regular schools and society; yet the process is quite restrained and measured, causing many to suggest that there must be a way and means to accelerate this process. If only Latvians could access and use practices found (Alberta) Canada or another inclusive country (Finland), that has successfully integrated students and adults with disabilities into school and society, to diminish Latvian problems such as life long dependency, poverty and social exclusion that adds to an already existing stigma of intellectual disability according to the European Union Monitoring and Advocacy Program (EUMAP, 2005). Stigma is the one issue that keeps surfacing as the key challenge for people with special needs in Latvia (Fine-Davis & Faas, 2014). Latvian society at present has minimal exposure and experience with children and adults with special needs, resulting in unawareness, avoidance, and a general misunderstanding of this population.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.351
Teacher spread0.326 · 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 teacher head, not a consensus.

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

Citations6
Published2018
Admission routes2
Has abstractyes

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