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Record W2608735306

Re-imagining Teacher Identity for the Creative Economy: Establishing an Ethos of Creative Teaching in Canada

2017· article· en· W2608735306 on OpenAlexaffabout
Layal Shuman, Sean Wiebe, Mitchell McLarnon, Patrick Howard, Mindy Carter, Pamela Richardson, Peter Gouzouasis, Kathryn Ricketts

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsRoyal Roads UniversityUniversity of Prince Edward IslandCape Breton UniversityUniversity of British ColumbiaUniversity of ReginaMcGill University
Fundersnot available
KeywordsEthosCreativityPedagogyIdentity (music)SociologyPublic relationsMathematics educationPolitical sciencePsychologySocial psychologyAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Looked at broadly, much of education is focused on skills training that rarely stimulates students' creativity and critical capacities (Digital Economy Research Team, 2011-2014). In some schools, there are teachers and students who are committed to creativity as a core practice, but these are the exceptions. Schools in general lack the entrepreneurial systems and infrastructure that could transform them into hubs for social innovation. In this symposium, we share our Pan Canadian research that addresses the creativity problem. In six Canadian research sites we have collaborated with teachers and established an ethos of creative practice in their schools. In helping teachers confront the difficulties of implementing creative pedagogy, we outline how a/r/tography and design thinking enable teachers to engage in research while advancing their artistic and analytical practices.

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)0.009
metaresearch head score (Gemma)0.015
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 score0.256
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0500.019
Scholarly communication0.0150.004
Open science0.0030.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.117
GPT teacher head0.408
Teacher spread0.291 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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