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Record W4403737017 · doi:10.3390/su16219210

Educating in and for Degrowth: Training Future Generations to Prevent Environmental Collapse

2024· article· en· W4403737017 on OpenAlexaff
Enrique Javier Díez Gutiérrez, José Jesús Trujillo Vargas, Eva Palomo Cermeño, Ignacio Perlado Lamo de Espinosa, Luisa-María García-Salas, Kelly Romero-Acosta, Luis Miguel Mateos Toro, Antonio Pérez-Robles

Bibliographic record

VenueSustainability · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersEuropean Commission
KeywordsDegrowthTraining (meteorology)EconomicsEnvironmental ethicsSustainabilityEcologyEngineeringGeographyBiologyPhilosophyMeteorology

Abstract

fetched live from OpenAlex

This research has been developed through a literature review on the importance of and current approach in the education system to the present environmental and ecosystemic crisis and the training of future generations in degrowth in the Spanish education system. To this end, a systematic literature review (SLR) has been carried out following the standards of the PRISMA declaration. In total, 40 articles published between January 2005 and March 2024 were selected from the following databases: Scopus, Dialnet, Web of Science and Scielo. The findings show it is a relevant topic in school education as a concern, but it is not reflected in educational practice; that it has been incorporated into the curriculum, but sporadically, decontextualised and more focused on ‘sustainable development’; also, it lacks critical questioning of the unlimited growth and consumption model that capitalism entails. The study concludes that it is crucial to incorporate degrowth in a transversal way in education at all schooling levels, and to reform the curricula of the faculties of education in all universities so that the pedagogy of degrowth is a priority in the training of future teachers.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.346

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 science0.0000.000
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 head0.237
GPT teacher head0.464
Teacher spread0.228 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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