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Record W3212119608 · doi:10.7202/1083434ar

Embracing Ambiguity: The intersection of biology, music, and art in secondary school teaching for student creativity

2021· article· en· W3212119608 on OpenAlexaffvenue
Tasha Ausman, Travis Mandel

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsCarleton UniversityCentrale des Syndicats du QuébecUniversity of Ottawa
Fundersnot available
KeywordsCreativityAmbiguityMetaphorIntersection (aeronautics)ConstructiveInterpretation (philosophy)Mathematics educationRelation (database)Field (mathematics)PsychologyPedagogySociologyComputer scienceLinguisticsSocial psychologyPhilosophyMathematics

Abstract

fetched live from OpenAlex

In these conversational field notes, two teachers reveal their experiences with creativity in contexts where students are encouraged to dwell in spaces of ambiguity and vulnerability in learning. Using anatomy to inform music pedagogy empowers students to work through metaphor-rich instruction in order to develop a grounded approach to artistic interpretation, while using fine art in the science classroom allows students of anatomy to explore the artistic possibilities of imagination in relation to the human body. In both cases, the crisscrossing of pedagogical lines from biology into music and music into art helped to transform students’ relationships with ambiguity from being negative and closed-off, to positive and constructive.

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)0.008
metaresearch head score (Gemma)0.012
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 score0.018
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0180.026
Scholarly communication0.0150.008
Open science0.0020.015
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0030.000

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 head0.283
GPT teacher head0.432
Teacher spread0.149 · 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
Published2021
Admission routes2
Has abstractyes

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