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Record W4224239994 · doi:10.5937/socpreg56-35802

Research of obstacles in inclusion of forced migrant children in the primary education in Serbia

2022· article· en· W4224239994 on OpenAlexaboutno aff
Marija Golubović

Bibliographic record

VenueSocioloski pregled · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)RefugeeObstacleQuarter (Canadian coin)Forced migrationWork (physics)Displaced personPedagogySociologyPolitical scienceGender studiesGeographyEngineering

Abstract

fetched live from OpenAlex

Since 2015, as many as 1.5 million migrants and refugees have passed through Serbia, whereas children account for one quarter of that number. Due to their longer stay in the territory of Serbia, a need has arisen, among other things, to include them in the education system. This research was aimed at determining factors that lead to the creation of obstacles in the inclusion of forced migrant children in primary education in Serbia, as well as during the process of education and, thus, how to overcome those obstacles in practice. Through semi-structured interviews, data were collected about the experience in teaching work with forced migrant children in Primary School "Ljupče Španac" in Bela Palanka. The participants of the research were the teachers of this school who during the academic 2018/19 year for the first time gained experience in working with forced migrant children. The research results show that greater system support is necessary for including forced migrant children in education, while an outstanding obstacle is the language barrier - in communication with children, but also with parents.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.401
Teacher spread0.370 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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