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Record W2097942784 · doi:10.3390/su5041356

From Talloires to Turin: A Critical Discourse Analysis of Declarations for Sustainability in Higher Education

2013· article· en· W2097942784 on OpenAlexaff
Paul Sylvestre, Rebecca McNeil, Tarah Wright

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSustainabilityIdeologyHigher educationCritical discourse analysisShadow (psychology)Work (physics)Political scienceSummitSociologySustainable developmentSet (abstract data type)Reading (process)Earth SummitSustainability organizationsEngineering ethicsEpistemologyPoliticsComputer scienceEngineeringLawGeographyPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Declarations for sustainability in higher education are often seen as a set of guiding principles that aid institutions of higher learning to incorporate the concept of sustainability into their various institutional dimensions. As the Decade of Education for Sustainable Development draws to a close and in the shadow of the 20th anniversary of the Earth Summit in Rio de Janeiro, it seems appropriate to re-evaluate how these declarations have changed over the past two decades. In this study, we apply critical discourse analysis to examine how sustainability and the university are socio-politically constructed within these documents. Our analysis uncovers evidence of ideological assumptions and structures that are potentially misaligned with notions of sustainability often discussed in the Sustainability in Higher Education (SHE) literature. It is not the purpose of this study to provide a definitive reading of the documents, but rather to ply a novel critical lens to help elucidate how some taken-for-granted assumptions present in the declarations may work against their stated goals.

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)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.431
Teacher spread0.403 · 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.

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

Citations45
Published2013
Admission routes1
Has abstractyes

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