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Record W4408837927 · doi:10.1080/15505170.2025.2478586

‘Design justice’ and transformative pedagogy: Experiments in globally connected learning with the global classroom for democracy innovation

2025· article· en· W4408837927 on OpenAlexaff
Matthew Wingfield, Jesi Carson, Joseph Mukisa Mujulizi, Marco Adamovic, Laurence Piper, Bettina von Lieres, Wilma Lundqvist Westin

Bibliographic record

VenueJournal of Curriculum and Pedagogy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsTransformative learningDemocracyEconomic JusticeSociologyPedagogyMathematics educationPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The Global Classroom for Democracy Innovation (GCDI) explores the impact that critical design frameworks like ‘design justice’ can have on student experiences and capacity building. Positioning students and civil society partners as co-creators and co-designers, while designing spaces that invite lived experience and facilitate collaborative work, can offer new pathways to reanimate the higher education learning environment. These experiences, particularly when engaging with “wicked problems”, can also be transformative. However, higher education systems, along with any intervening design frameworks, must be folded into an iterative praxis to ably support justice-oriented work. This paper is based on our experiences managing an internationally collaborative learning project spanning four continents and offers practical insights for educators interested in reimaging the function and form of higher education.

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)0.013
metaresearch head score (Gemma)0.029
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0050.006
Open science0.0020.007
Research integrity0.0030.004
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 head0.031
GPT teacher head0.411
Teacher spread0.379 · 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
Published2025
Admission routes1
Has abstractyes

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