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Record W2408679774 · doi:10.55016/ojs/ajer.v61i4.56155

Sufficiently Well Informed and Seriously Concerned? European Union Policy Responses to Marginalisation, Structural Racism, and Institutionalised Exclusion in Early Childhood

2016· article· en· W2408679774 on OpenAlexvenueno aff
Mathias Urban

Bibliographic record

VenueAlberta Journal of Educational Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersEuropean CommissionUNICEFWorld Bank Group
KeywordsRacismEuropean unionPsychologyCriminologyRacial biasSociologyDevelopmental psychologySocial psychologyGender studiesEconomics

Abstract

fetched live from OpenAlex

Throughout the European Union, children from marginalised communities experience an appalling reality of poverty, exclusion, discrimination, and racism. Growing up in poverty and social exclusion shapes the reality of the lived experience for an increasing number of children in one of the wealthiest regions of the world. In the UK, a member of the G7, a significant number of children suffer from hunger, malnutrition, and cold (Lansley & Mack, 2015) while the government has abandoned child poverty reduction targets; in Croatia, a recent accession to the EU, “it is normal that Roma children are mostly sick,” according to a recently published report (Šikić-Mićanović, Ivatts, Vojak, & Geiger-Zeman, 2015, p. x). Rather than examining the situation in specific countries, in this paper I undertake a critical inquiry into policy approaches and responses to inequality at the level of the European Union–including the EU Framework for National Roma Integration Strategies–with a specific focus on early childhood education, care, and development. However, while the policies put in place by the European Union have to be welcomed, they represent only one aspect of a complex and often contradictory picture. Perspectives from professionals and activists working “on the ground” are necessary to complement the official picture; they will be presented and discussed in order to identify systemic challenges. I conclude by making the case for a radical systemic turn in EU early childhood policies and for learning with and from experiences in so-called developing countries as a way forward to address these challenges.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.045
GPT teacher head0.391
Teacher spread0.346 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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