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Record W4404531709 · doi:10.1080/03057925.2024.2429819

Sustainability as an emerging mandate in higher education

2024· article· en· W4404531709 on OpenAlexafffund
Elizabeth Buckner, Zhang You

Bibliographic record

VenueCompare A Journal of Comparative and International Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMandateSustainabilityEnvironmental planningBusinessPolitical scienceEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

Universities worldwide have recently made commitments to advancing sustainability and sustainable development. However, much of the literature on higher education for sustainable development (HESD) is practice-oriented or prescriptive. This article seeks to explain how and why universities are framing their sustainability commitments. Drawing on semi-structured interviews with sustainability professionals at 33 universities in 19 countries, we find two overarching discursive rationales for university engagement with sustainability, which also shape their views on sustainability rankings and assessments: 1) sustainability as an internally oriented organising principle that shapes approaches to campus-based activities and 2) sustainability as part of an externally oriented stance, namely a broader international orientation linked to recognition and solving global problems. Drawing on sociological concepts of legitimacy and distinction, we argue that visible commitments to sustainability support universities’ claims to both moral and cognitive legitimacy, and global status, while also opening them up to criticism over authenticity and greenwashing.

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)0.022
metaresearch head score (Gemma)0.022
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.057
Scholarly communication0.0130.012
Open science0.0010.013
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.501
Teacher spread0.401 · 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
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

Citations11
Published2024
Admission routes2
Has abstractyes

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Same venueCompare A Journal of Comparative and International EducationSame topicSustainability in Higher EducationFrench-language works237,207