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Record W4380894310 · doi:10.30525/2500-946x/2023-1-12

WORLD EXPERIENCE OF UNIVERSITY SUSTAINABLE DEVELOPMENT

2023· article· en· W4380894310 on OpenAlexaboutno aff
Наталія Холявко, Iryna Didenko

Bibliographic record

VenueEconomics & Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentHigher educationEconomic growthPolitical scienceOrder (exchange)BusinessEconomics

Abstract

fetched live from OpenAlex

Introduction. A typical practice of the world's leading countries is the transformation of higher education institutions into agents of change in society. In the XXI century, these changes are primarily focused on various aspects of sustainable development of the country and its regions. Currently, it is the universities, together with NGOs, that have the greatest impact on the achievement of the Sustainable Development Goals proclaimed by the UN for 2015. The purpose of the research is to analyse the global experience of sustainable development in higher education institutions (HEIs). Methodology. This study used the cognitive method of analysis. In order to obtain the most objective research results, the authors studied the universities included in the international Times Higher Education Impact Rankings. The study covered universities from all over the world that were included in the top 50 of the rating. The results of the analysis are systematised according to geography (the article includes several sections characterising the sustainable development of universities in Europe, the United States, Canada and Australia). Results. Higher education institutions are now expected to become leaders in sustainable change in the country, economy and society. The world's leading universities are demonstrating how to progressively transform their activities in line with sustainable principles. They are investing heavily in the implementation of the latest technologies for energy saving, water conservation, campus landscaping and waste recycling. Since they have access to areas where the natural complex is preserved, universities are trying to support these areas and create favourable conditions for using them as living laboratories in educational and research processes. Universities offer sustainable development and lifestyles as part of their educational activities (public lectures, expert workshops, specialised short-term online training, etc.). Universities influence the achievement of the Sustainable Development Goals through their educational and research (inventive) activities. The world's leading universities are keen to promote the concept of sustainable development: they share events held, projects and initiatives undertaken and goals achieved widely on their official websites and social networks. Conclusion. The main directions of sustainable development in higher education institutions are 1) sustainable development of the campus (carbon neutrality, rational consumption, energy efficiency, waste recycling, optimisation of drinking water consumption, green transport, food safety for students); 2) sustainable educational programmes and courses (a sustainable component in students' bachelor and master theses); 3) sustainable research (innovative technologies against climate change, water conservation, energy saving, etc.); 4) management (internal regulatory documents on sustainable development, specialised sustainability centres to promote and support sustainable initiatives). Long-term partnership with stakeholders (entrepreneurs, local authorities and students as agents of future sustainable change) plays an important role in ensuring sustainability. The sustainable development strategy of a modern higher education institution should be based on the principles of complexity and coherence, which will not allow sustainability measures and initiatives to be fragmented and asynchronous. Areas for further research include building a theoretical and methodological framework for the development of an integrated ecosystem of sustainable development of universities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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