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Record W2101673252 · doi:10.3390/su7010725

Canadian STARS-Rated Campus Sustainability Plans: Priorities, Plan Creation and Design

2015· article· en· W2101673252 on OpenAlexafffundabout
Lauri Lidstone, Tarah Wright, Kate Sherren

Bibliographic record

VenueSustainability · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsSustainabilityTimelineSustainability organizationsStakeholderSocial sustainabilityPlan (archaeology)BusinessWork (physics)Process (computing)Higher educationAction planSustainability scienceProcess managementEnvironmental planningEnvironmental resource managementPublic relationsPolitical scienceEngineeringEconomic growthComputer scienceManagementEconomicsGeography

Abstract

fetched live from OpenAlex

The use of integrated sustainability plans is an emerging trend in higher education institutions (HEIs) to set sustainability priorities and to create a work plan for action. This paper analyses the sustainability plans of 21 Canadian HEIs that have used the Sustainability Tracking, Assessment and Rating System (STARS) from the Association for the Advancement of Sustainability in Higher Education (AASHE). The plans were coded thematically with a focus on the sustainability goals, process of plan creation, and aspects of plan design outlined in the texts. This paper finds that sustainability goals focused on the environmental aspects of sustainability, while social and economic aspects were less emphasized. Further, most plans were described as being created through a broad stakeholder-consultation process, while fewer plans assigned timelines and parties responsible to sustainability goals. This paper contributes to our understanding of the priorities of Canadian HEI institutions at the end of the Decade of Education for Sustainable Development and is useful for practitioners interested in developing their own sustainability plans.

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)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.331
Teacher spread0.300 · 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

Citations42
Published2015
Admission routes3
Has abstractyes

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