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Record W3046567164 · doi:10.17169/refubium-27617

Universities as Change Makers

2020· article· en· W3046567164 on OpenAlexaboutno aff
Bettina Schorr, Katrin Schweigel, Frauke Berg, María Alejandra Cuentas

Bibliographic record

VenueRefubium (Universitätsbibliothek der Freien Universität Berlin) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsGlobeSustainabilityBusinessPolitical science

Abstract

fetched live from OpenAlex

Higher Education Institutions (HEI) play a strategically important role in the multidimensional transformations needed to achieve more sustainable ways of living in this world. By applying a holistic or “whole institution approach,” they can promote and implement sustainability in research, teaching, campus management, and carry out the “third mission” of universities to generate trans-disciplinary knowledge useful for society. This publication showcases various projects and initiatives developed by universities from Canada, Chile, China, Colombia, Germany, Israel, Mexico, Peru, and Russia to promote the topic of sustainability in governance, teaching, research, and campus management. By presenting the key issues, recommendations, and lessons learned from these initiatives, it outlines innovative and diverse approaches that contribute to fostering sustainability at HEIs. These cases highlight experiences from all over the world that can be adopted by interested HEIs anywhere, in particular those HEIs forced to operate under conditions of serious resource scarcity and in contexts where sustainability is not yet a major part of academic activities. We also hope that this publication will be the start for closer exchanges on sustainability initiatives among universities all over the world.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0000.008
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.068
GPT teacher head0.335
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

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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