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Record W4382771468 · doi:10.1080/1554480x.2023.2230954

Mapping the landscape of an ICE community of learnership

2023· article· en· W4382771468 on OpenAlexaffabout
Dany Dias, Blaine E. Hatt

Bibliographic record

VenuePedagogies An International Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsSt. Francis Xavier UniversityNipissing University
Fundersnot available
KeywordsDialogicCreativityPraxisPedagogyCurriculumSociologyCuriosityContext (archaeology)Space (punctuation)Mathematics educationPsychologyGeographySocial psychologyEpistemology

Abstract

fetched live from OpenAlex

Drawing upon the foundational principles of Imagination Creativity Education (ICE), our article examines the relational dimension in the context of classroom environments, as observed within the Canadian schooling system. We explore the landscape of a community of learnership – the lived and living practices of both students and teacher in the communal space in between, a space characterized as the transactional curriculum. We argue that teachers play a vital role in cultivating intentional relationships with students, one whose pedagogical responsibility focuses on establishing relationships, belonging, and dialogue; viewing students holistically for who they are and who they have the potential of becoming. Teachers who design such communities of learnership subscribe to pedagogical praxis, attending to their students’ lived curricula in which deep, engaged, and innovative learning is enacted. In this article, we outline the principles of ICE, through which a community of learnership can be forged. Grounded in Freire’s (1997) concept of gnoseologica of education, we expand on dialogic relationships that value curiosity, imagination, creativity, and innovation. We draw on one author’s Grade 8Footnote1 classroom inquiry to describe the lived experiences of an ICE community of learnership.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.126
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.241
GPT teacher head0.444
Teacher spread0.203 · 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 teacher head, 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

Citations0
Published2023
Admission routes2
Has abstractyes

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