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Towards a Sustainable Model of Higher Education in the Araucanía Region, Chile

2020· article· en· W3035918912 on OpenAlexaff
César Jesús Vázquez Navarrete, G Saldías, María de los Ángeles Carbonetti, Juan Manuel Fierro, Doménica Sandoval

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSustainabilityPromotion (chess)Sustainable developmentContext (archaeology)SociologyWork (physics)Sociocultural evolutionHigher educationEducation for sustainable developmentPolitical sciencePedagogyLearning communityEngineering ethicsEngineeringGeographyPolitics

Abstract

fetched live from OpenAlex

Abstract The aim of this article is to propose a critical reflection of the state of teaching sustainability in higher education in Chile. The goal is to broaden the concept of sustainability so as to understand its implication in learning civic and community values, as well as in implementing innovative teaching methodologies linked to the promotion of sustainable values connected to the United Nations’ Sustainable Development Goals. In this article, the application of a teaching methodology called Pre-Texts , from Harvard University, is explored in a sociocultural context of Chilean higher education, specifically at the Universidad de La Frontera in Chile’s 9 th Region. Evidence is sought about how this methodology is relevant and effective in the teaching of two key pedagogical principles related to sustainability: the importance of community recognition and the collective work and implementation of artistic practices to promote discussion of sustainable principles based on preservation and recycling. Finally, a conclusion will be pursued around the need to implement these and similar teaching practices in higher education to promote greater awareness of the fundamental principles of Sustainable 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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