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Record W2197102844

Are Canadian Universities Taking Sustainability Seriously? A Case Study Analysis of Sustainability Initiatives at Three Canadian Campuses and the Lessons Decision-Makers Can Learn from These Efforts

2010· article· en· W2197102844 on OpenAlexaffabout
Daniel Rosenbloom

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSustainabilitySustainability organizationsHigher educationSustainability sciencePolitical scienceOrder (exchange)Public relationsSocial sustainabilityPublic administrationEngineering ethicsBusinessEngineering
DOInot available

Abstract

fetched live from OpenAlex

This study attempts to answer the question ‘are universities taking sustainability seriously’? Specifically, this paper examines the extent higher education institutions have institutionalized sustainability into their decision-making processes. It investigates whether universities have incorporated a comprehensive conception of sustainability into visioning, planning, operations, and administration. It also attempts to determine how educational institutions have gone about institutionalizing sustainability and glean best practices that may be applied in other institutional settings. In order to answer the above questions, I present three case studies of sustainability policies and efforts at the following Canadian universities: The University of British Columbia, The University of Calgary, and Carleton University. These case studies examine the plans, actions, and progress at each university to determine if they sufficiently address sustainability considerations. Case studies also attempt to take stock of campus sustainability efforts and highlight achievements and deficiencies. Subsequently, the experience of universities is utilized to uncover and discuss lessons for policymakers and barriers to sustainability. Finally, recommendations are made for overcoming barriers.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.011
GPT teacher head0.320
Teacher spread0.309 · 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

Citations4
Published2010
Admission routes2
Has abstractyes

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