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Record W4399916351 · doi:10.1007/978-3-031-56172-6_15

Weaving Curriculum, Assessment, and Pedagogy: Global Citizenship Experience Lab School’s Whole-School Approach to Sustainability and Global Citizenship Education

2024· book-chapter· en· W4399916351 on OpenAlexaff
Stephanie Leite

Bibliographic record

VenueSustainable development goals series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsCitizenshipCurriculumSustainabilityGlobal citizenshipPedagogyCitizenship educationPolitical scienceSociologyMathematics educationPsychology

Abstract

fetched live from OpenAlex

Abstract This chapter presents a case study of Global Citizenship Experience Lab School in Chicago, USA—a secondary school dedicated to integrating curriculum, assessment, and pedagogy to promote real-world experiential learning. The chapter analyzes the school’s use of scaffolding to introduce students and teachers to a project-based learning model and examines the school’s commitment to global citizenship as an entry point for a whole-school approach to sustainability. Theoretical contributions to the field of education for sustainable development recommend integrated, whole-school approaches to sustainability. However, schools transitioning to such a holistic model face many challenges due to the wider systems in which they are embedded. This case study exemplifies how approaching school redesign as a process of simultaneously rethinking curriculum, assessment, and pedagogy may instill a more relational way of thinking, which is essential if we are to transcend and transform the social and ecological crises of today.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.018
GPT teacher head0.353
Teacher spread0.335 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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