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Record W4220862695 · doi:10.1080/1350293x.2022.2055099

Children’s perspectives about belonging in educational settings in five European countries

2022· article· en· W4220862695 on OpenAlexaff
Jóhanna Einarsdóttir, Jaana Juutinen, Anette Emilson, Sara M. Ólafsdóttir, Berit Zachrisen, Sarah Meuser

Bibliographic record

VenueEuropean Early Childhood Education Research Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsEducation and Early Childhood Development
FundersNordForsk
KeywordsFriendshipEarly childhood educationGender studiesDevelopmental psychologyEarly childhoodPoliticsSociologyChildhood studiesPsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The article addresses children’s perspectives of belonging in early childhood education settings in five European countries. Children’s belonging is understood to be dynamic processes that are formed through multiple relations. Twenty children aged 4–8 with diverse backgrounds participated in the study: two boys and two girls from each country. Data were constructed through walking interviews, during which the individual children walked around the ECE settings with the researcher, taking photos and conversing at the same time. Yuval-Davises’ [2011. The Politics of Belonging: Intersectional Contestations. London: SAGE] three facets of belonging were utilised as a theoretical lens to analyse the data and to shed light on the findings. The findings illustrate how belonging, from the participating children’s perspectives, is strongly related to friendship, being surrounded by caring adults and being a member of the ECE community.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.007
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.347
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 source (direct Gemma or distilled Codex), 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

Citations25
Published2022
Admission routes1
Has abstractyes

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