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Record W4399916379 · doi:10.1007/978-3-031-56172-6_18

Practicing Climate Action in a K-12 School Using a Whole Institution Approach

2024· book-chapter· en· W4399916379 on OpenAlex
Kristen Hargis

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 institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInstitutionAction (physics)Mathematics educationSociologyPsychologyPhysicsSocial science

Abstract

fetched live from OpenAlex

Abstract Most climate change education and research have focused on instilling individual scientific cognitive clarity instead of learning how to take climate actions together. This study utilized a practice lens to adjust focus away from an individual learner’s knowledge to the climate action practices collectively “carried,” (un)equally shared, and mutually shaped. Shove and colleagues’ conceptualization of practices was used to examine climate action practices at a Kindergarten to Grade 12 school in Canada using a whole institution approach to climate change education. A whole institution approach includes climate change education within and across each domain of Overall Governance, Teaching and Learning, Community Partnerships, and Facilities and Operations. Methods included a sensory walk, observations, interviews, focus groups, document collection, and photography. This chapter describes key themes identified within each of the whole institution domains with implications for a whole institution approach to CCE overall, as well as for each domain.

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 score0.923
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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.262
Teacher spread0.240 · 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