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Record W4415224995 · doi:10.15173/ijsap.v9i2.6013

Establishing local sustainability projects that address the UN Sustainability Development Goals

2025· article· en· W4415224995 on OpenAlexvenueno aff
Diana J. Pritchard, Vicky Morris, Irina-Petruta Balanoiu

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsNature versus nurtureSustainabilityGeneral partnershipExperiential learningCurriculumFraming (construction)CreativitySustainable developmentSustainability organizations

Abstract

fetched live from OpenAlex

Before increasing global disruptions and uncertainties, it is necessary to prepare students with capabilities to influence change and to nurture their agency. This case study examines a pilot student sustainability leadership initiative run at an English university. It comprises a model which combines student as partners and “living lab” practices. It engaged undergraduates in partnership with academics in projects they co-created to address curriculum and campus challenges, framing these in relation to the UN Sustainability Development Goals. The model, underpinned by constructivist and experiential learning pedagogies, harnesses creativity to nurture action for sustainability. A multi-level evaluation identified the impacts of the experience on students and academics and of the outputs in relation to the university’s education and sustainability strategies. The results demonstrate this student as partner and living lab model to be effective and efficient. It has been adopted as business-as-usual at the university and is transferable. This case study is co-authored by the staff and some of the students involved.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.022
GPT teacher head0.431
Teacher spread0.410 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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