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Record W4409864879 · doi:10.37602/ijrehc.2025.6233

THE TRANSFORMATION OF SCHOOL SPACE IN EUROPE AFTER WORLD WAR II

2025· article· en· W4409864879 on OpenAlexaff
APOSTOLOS KARAOULAS

Bibliographic record

VenueInternational Journal of Research in Education Humanities and Commerce · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Education Studies Worldwide
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsTransformation (genetics)Space (punctuation)World War IIPolitical sciencePhilosophyLawLinguistics

Abstract

fetched live from OpenAlex

The study of the role of school infrastructure in the educational process constitutes a critical field for understanding transformations in both education and European society. Beyond their physical nature, school facilities play a significant role in shaping the learning environment and the educational process itself. Following World War II, Europe faced the urgent need to restructure its educational systems, with school architecture emerging as a crucial factor in this endeavor. The architectural and technological innovations integrated into school buildings reflected the pedagogical theories of the time, which emphasized creativity, collaboration, and the development of critical thinking. The modern school space, through the combination of sustainability and innovative pedagogical practices, mirrors social progress and the values of the era, preparing students for the complex challenges of the modern world. The interaction between school space and the learning process demonstrates that education is not confined to the transmission of knowledge but is primarily an act of building values, fostering cooperation, and enabling social interaction. By examining the evolution of school infrastructure and its connection to educational policies, the article highlights the importance of school space design as a determining factor in enhancing learning, sociability, and students’ personal development.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.083
GPT teacher head0.447
Teacher spread0.364 · 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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