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Record W4413315429 · doi:10.14507/epaa.33.8711

Challenges to the education rights of children on the move in Latin America: A scoping review

2025· article· en· W4413315429 on OpenAlexaff
Claudia Díaz Ríos, Tatiana Britto, Gisele Cuglievan-Mindreau, Sana Abuleil, Indira Quintasi-Orosco

Bibliographic record

VenueEducation Policy Analysis Archives · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImmigration and Intercultural Education
Canadian institutionsUniversity of Toronto
FundersOffice of International Science and EngineeringPontificia Universidad Católica del Perú
KeywordsLatin AmericansPolitical scienceEconomic growthRight to educationSociologyPedagogyPublic administrationHuman rightsEconomicsLaw

Abstract

fetched live from OpenAlex

Research on education in emergencies underscores the significant structural obstacles refugee children encounter in accessing education within low- and middle-income countries. However, there remains a notable gap in understanding the challenges confronted by transnational migrant children and the evolving nature of these challenges amidst shifting migration dynamics. To address this gap, we conducted a scoping literature review of 144 articles written in Spanish, Portuguese, and English examining transnational migration and education in Latin America, a region undergoing profound shifts in immigration patterns. Our analysis reveals that changes in these patterns activate new challenges for education rights. As migrants gradually relocated to other Latin American countries, their children encountered barriers to education compounded by instances of assimilation and discrimination within schools. With migration waves intensifying, discrimination evolves, influenced by governance structures fostering diversity but enabling physical segregation and ongoing marginalization of migrant children. These findings anticipate challenges in implementing current policy recommendations for integrating refugees and other groups of children on the move into the regular education systems of Global South 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.409
Teacher spread0.379 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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