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Record W4410510619 · doi:10.37602/ijrehc.2025.6302

SCHOOL FAILURE IN EUROPEAN EDUCATION: FROM THE STUDENT TO THE EDUCATIONAL SYSTEM

2025· article· en· W4410510619 on OpenAlexaff
APOSTOLOS KARAOULAS

Bibliographic record

VenueInternational Journal of Research in Education Humanities and Commerce · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMathematics educationPolitical sciencePedagogyPsychology

Abstract

fetched live from OpenAlex

School failure has been a central concern of European educational policy for decades, as it is closely linked to the social, economic, and ideological transformations of each era. In the past, failure was often interpreted in terms of individual inadequacy or lack of effort approaching school achievement as the result of innate abilities and personal will. However, as early as the 1960s, research began to highlight the decisive role of social capital and class position in academic success, challenging the dominant narrative of individual responsibility. The shift towards a more systemic approach has been reinforced in recent decades, with European Union policies focusing on preventing student dropout and reducing educational inequalities. The concept of school failure has been transformed from an individual issue into an indicator of the structural weaknesses of the educational system, acknowledging the influence of factors such as educational policy, social background, and the school environment. This article explores the transition to the current understanding of school failure through a historical and political analysis of European education, focusing on the educational policies that have shaped the perception of the phenomenon. The analysis centers on the theoretical dimensions of school failure, educational inequalities, and policy interventions aimed at addressing the issue, examining the strategies and tools developed to reduce disparities and enhance access to education for all.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.391
Teacher spread0.350 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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