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Record W4404083629 · doi:10.1177/17461979241289280

Pedagogical principles for encouraging (socially just) youth climate action: A schema for citizenship education curriculum analysis

2024· article· en· W4404083629 on OpenAlexaffabout
Rebecca S. Evans, Heather E. McGregor, Brenda Reed

Bibliographic record

VenueEducation Citizenship and Social Justice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsQueen's University
Fundersnot available
KeywordsCurriculumCitizenship educationCitizenshipPedagogySociologySchema (genetic algorithms)Action (physics)Action researchEnvironmental educationCurriculum developmentMathematics educationPsychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article synthesizes pedagogical principles for supporting youth climate action from across local (Ontario) and global literature. It also seeks out stories of Indigenous youth engaging in climate action in distinct ways—highlighting examples from Sioux (of Standing Rock) and Inuit youth. The authors propose the Pedagogical Principles for Supporting Climate Action curriculum analysis schema before using it to examine the Ontario citizenship framework. The findings reveal how the curriculum segment in its current form is incongruent with the pedagogical principles of supporting youth climate action. The authors articulate both immediate and urgent curriculum revisions that are necessary for supporting youth in socially just climate action—with a call for immediate revisions that include a shift toward centering Indigenous knowledges, integrating climate justice and upholding relationality to the land and all that it sustains.

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)0.023
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.169
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0100.040
Scholarly communication0.0100.005
Open science0.0030.006
Research integrity0.0030.005
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 head0.179
GPT teacher head0.453
Teacher spread0.274 · 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 designQualitative
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

Citations3
Published2024
Admission routes2
Has abstractyes

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