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Record W2299225627 · doi:10.1186/s40552-016-0016-5

A bibliometric study on “education for sustainability”

2016· article· en· W2299225627 on OpenAlexaboutno aff
Pedro Luiz Côrtes, Rosely Valéria Rodrigues

Bibliographic record

VenueBrazilian Journal of Science and Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilitySubject (documents)Maturity (psychological)BibliometricsSocial sciencePolitical scienceLibrary scienceRegional scienceSociologyEcology

Abstract

fetched live from OpenAlex

The scientific production on “education for sustainability” has been growing in recent years what demonstrates the attention this subject has gathered. To better understand and characterize this trend, a bibliometric study of international papers on the subject was developed. The results show that the production has been growing since the middle of the last decade, focusing on the field of the applied social sciences, environmental sciences, energy, management, engineering, humanities and psychology. Australia, United Kingdom, United States, New Zealand, Spain, Israel and Canada are the countries that stand out, and it was possible to characterize the evolution of the production in each of these countries in the last 10 years, as well as to indicate the most used journals, the associated sub-themes, the most cited papers, the most productive authors and their affiliations. This allowed us to understand how the research on “education for sustainability” is being developed, showing its level of maturity and the most frequent themes and journals that have published more papers in the area. It was also possible to identify some themes which present research opportunities. The practical results of this study serve as a guideline for researchers, helping them to explore the available bibliography and the better ways to convey their production.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.019
Science and technology studies0.0000.002
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 head0.009
GPT teacher head0.300
Teacher spread0.291 · 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 designObservational
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

Citations29
Published2016
Admission routes1
Has abstractyes

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