MétaCan
Menu
Back to cohort
Record W2162187479 · doi:10.3390/su5052252

Greening the Ivory Tower: A Review of Educational Research on Sustainability in Post-Secondary Education

2013· review· en· W2162187479 on OpenAlexafffund
Philip Vaughter, Tarah Wright, Marcia McKenzie, Lauri Lidstone

Bibliographic record

VenueSustainability · 2013
Typereview
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsDalhousie UniversityUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIvory towerSustainabilityGreeningPolitical science

Abstract

fetched live from OpenAlex

There is a deficit of multi-site studies examining the integration of sustainability in the policies and practices of post-secondary institutions. This paper reviews what comparative empirical research has been undertaken on sustainability in post-secondary education (PSE) within eight leading international journals publishing on sustainability and education. Three predominant themes of research on the topic are identified within the review: research comparing sustainability curricula across institutions (both within specific disciplines of study and across disciplines); research comparing campus operations policies and practice across multiple institutions; and research on how to best measure or audit approaches and outputs in sustainability in PSE. This review of the research literature supports the contention within the literature on sustainability in PSE that most research on the topic is focused on case studies rather than comparison of multiple institutions. The comparative research that is emerging from the field is concentrated on assessing measurable outputs for environmental externalities within institutional operations, with little examination of sustainability uptake and outcomes across broader institutional policies and practices.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.013
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.099
GPT teacher head0.520
Teacher spread0.421 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations103
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueSustainabilitySame topicSustainability in Higher EducationFrench-language works237,207