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Record W4395084894 · doi:10.36443/9788418465826

Integrative models of Education for Citizenship (Handbook) - 2nd edition

2024· book· en· W4395084894 on OpenAlexaff
Miguel Corbí Santamaría, Eva María García Terceño, Almudena Alonso Centeno, Ileana M. Greca, Delfín Ortega Sánchez, Jairo Ortiz-Revilla, Esther Sanz de la Cal, Jakub Lipták, Iveta Polák Čuchtová, Ewa Parucka, Monika Powęska, Kerstin Hansen, Tobias Sohr, Lise Olsen

Bibliographic record

VenueUniversidad de Burgos eBooks · 2024
Typebook
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizenshipPsychologyMathematics educationSociologyCognitive sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Developing active citizenship is essential for achieving social participation under principles such as mutual respect and non-violence, in accordance with human rights. Our society needs to educate individuals with decision-making abilities and critical thinking skills to address current issues. This second edition of the manual complements the first with some modifications to the mathematics chapter and includes a new chapter on the role of Physical Education in integrated models. In this way, the manual offers readers a more global perspective on what integrated education models are, advocating for directing efforts towards a more inclusive education concept and bringing education professionals closer to the concept of active citizenship and its application in the classroom. The experience of the first edition has shown us that this manual represents a magnificent starting point for developing innovative education alternatives with nuances of the competencies that our current society strongly demands.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0280.010

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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