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Record W3110970041 · doi:10.5206/eei.v30i3.13379

Newcomer Families’ Participation in Early Childhood Education Programs

2020· article· en· W3110970041 on OpenAlexaffvenueabout
Christine Massing, Daniel Kikulwe, Needal Ghadi

Bibliographic record

VenueExceptionality Education International · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsYork UniversityUniversity of Regina
Fundersnot available
KeywordsSocializationSocial capitalContext (archaeology)Qualitative researchCultural capitalFocus groupEarly childhood educationSociologyEarly childhoodPublic relationsPedagogyEconomic growthPsychologyPolitical scienceDevelopmental psychologySocial science

Abstract

fetched live from OpenAlex

Early childhood education and care (ECEC) programs are often the first point of contact that newcomers have with formal institutions in their new country, and function as sites wherein children can gain access to the language, rules, and customs of the dominant society. However, newcomer families may experience specific barriers to accessing programs due to the lack of recognition of their existing social and cultural capital within the host country. Reporting on part of a larger mixed-methods study focused on the post-migration barriers to integration experienced by newcomers, this article explores newcomer families’ perspectives on the issues affecting their participation in ECEC programs. Qualitative data were collected from 96 newcomers to Canada during 13 focus groups. The findings suggest newcomer families grapple with reconciling three main points of disjuncture or conflict associated with these child care decisions: social networks, necessity and opportunity, and socialization goals. While participation in ECEC programs facilitated the development of forms of capital valued in the new context, these families also felt compelled to shed some of their own aspirations for their children’s socialization and learning.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.361
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 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

Citations7
Published2020
Admission routes3
Has abstractyes

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Same venueExceptionality Education InternationalSame topicEarly Childhood Education and DevelopmentFrench-language works237,207