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Record W4399916373 · doi:10.1007/978-3-031-56172-6_7

Accelerating Change-Making: Reflections on Embedding Regenerative Practices in School Climate Action

2024· book-chapter· en· W4399916373 on OpenAlex
Ellen Field, M Andrews, John Hannah, Eleonor Kerr, D. A. Stephens, Alison Elliott

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSustainable development goals series · 2024
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsSchlumberger (Canada)Trinity CollegeLakehead University
Fundersnot available
KeywordsAction (physics)EmbeddingClimate changeSociologyGeologyComputer scienceOceanographyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This chapter explores how three schools in Canada are accelerating climate action through a whole school approach, facilitated by the Climate Action Accelerator Program (CAAP). Teachers and administrators from three different schools and at different stages (early, intermediate, and advanced) of whole school sustainability, engaged in critical reflection on their schools’ journeys, shared through three vignettes. The vignettes are followed by a discussion of shared approaches across the schools that we noted as important—including regenerative practice as a paradigm shift—for moving schools along climate action pathways and whole school journeys as well as shared challenges and emerging opportunities.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.088
GPT teacher head0.370
Teacher spread0.281 · 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