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Record W2101336570 · doi:10.2304/rcie.2008.3.3.281

Understanding Childhoods in-between: Sudanese Refugee Children's Transition from Home to Preschool

2008· article· en· W2101336570 on OpenAlexaffabout
Darcey Dachyshyn, Anna Kirova

Bibliographic record

VenueResearch in Comparative and International Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRefugeeMediationNegotiationDevelopmental psychologyEarly childhood educationEarly childhoodIdentity (music)PsychologyGender studiesSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Canada receives over 30,000 refugees each year, approximately 10% of whom are under five years of age. While to varying degrees the factors influencing the experiences of adult refugees have been identified and researched, the experiences of young refugee children ‘living in-between’ has only recently begun to capture researchers' interest. This article considers what the experiences are of young refugee children in their day-to-day living between languages and cultures as they make a transition between home and Canadian early childhood settings. More specifically, the question addressed is: What roles do refugee children play in mediating the host culture for their parents in the hybrid place created by play? The authors propose that play in early childhood does serve, for refugees experiencing resettlement, as a site of cultural mediation, contestation, and identity negotiation. An analysis of three Sudanese refugee mothers and their four-year-old sons' use of common early childhood artefacts – wooden building blocks – is used to demonstrate how young refugee children who experience child care outside their home for the first time not only learn to ‘be a preschooler’, but learn to ‘interpret’ this role to their 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.487
GPT teacher head0.511
Teacher spread0.024 · 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

Citations19
Published2008
Admission routes2
Has abstractyes

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