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INFORMATION SUPPORT FOR THE INTEGRATION OF UKRAINIAN REFUGEE SCHOOLCHILDREN INTO THE EDUCATION SYSTEMS OF DEVELOPED COUNTRIES

2024· article· en· W4405364915 on OpenAlexaboutno aff
Sergiy Londar, Oleksandr Bosenko

Bibliographic record

VenueEducational Analytics of Ukraine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeUkrainianPolitical scienceEquity (law)NoveltyEducational attainmentPublic relationsEconomic growthPsychologySocial psychologyEconomics

Abstract

fetched live from OpenAlex

The article examines an author-designed research framework for developing a standardized informational framework to facilitate the integration of Ukrainian refugee schoolchildren into the educational systems of developed countries such as the United States, Canada and Germany. In the developed countries, evidence-based policymaking is institutio­nalized at the legislative level, enabling relatively seamless adoption of such frameworks. The standardized informational support may incorporate components derived from pre-existing datasets on refugee schoolchildren within the national Education Management Information System (EMIS), specifically Ukraine’s Automated Information Complex of Educational Management (AICEM), alongside supplementary data gathered from surveys and analyses of the educational and socio-economic challenges confronted by Ukrainian refugee schoolchildren and their families in host countries. The study outlines a research design featuring a cross-sectional survey of schoolchildren, their guardians and educators. The questionnaires encom­pass three principal dimensions: socio-economic, informational and psycho-emotional. The methodological framework entails rigorous statistical analysis of anonymized, disaggre­gated data segmented by age, gender and educational attainment. The findings of this research will enable the identification of critical challenges and the formulation of evidence-based recommendations for educational institutions and policymakers in host countries, taking into account the opportunities for remote learning, gender equity and inclusivity. Moreover, the study’s outcomes will contribute to enhancing intercultural dialogue and the social integration of refugee schoolchildren, which are crucial factors for their academic success. The scientific novelty of the research lies in the development of an approach that will enable for the creation of a standardized informational framework for refugee schoolchildren, and thus, identify effective strategies for addressing their educational challenges and alleviating the socio-economic issues faced by refugee families and their school-aged children in the host countries.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.621

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.355
Teacher spread0.335 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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