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Record W4389941119 · doi:10.21810/sfuer.v15i1.6131

Integrating Artistic Knowing With Ancient STEM

2023· article· en· W4389941119 on OpenAlexaffvenue
Tanya Behrisch

Bibliographic record

VenueSFU Educational Review · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAestheticsVisual artsArt

Abstract

fetched live from OpenAlex

Integrating artistic practice into STEM to create STEAM offers learners intersubjective experience with subjects they study.This contrasts with traditional STEM education which upholds an ontological belief in human separation from the world they observe.This ontological separation justifies the absence of ethical considerations towards those subjects we study.In the context of our environmental crisis, this is problematic because humans are neither separate nor free from ethical ties to those we study.Rather, we are dependent on those who spark our curiosity and sustain our lives.Integrating artistic practices such as oil painting into STEM education brings intersubjectivity into scientific learning.Creating art depends on treating our affective and embodied responses to subjects as valid data and sources of knowledge.This brings learners into dialogue with fellow subjects and shifts from a traditionally objectifying stance towards one of kinship.The author demonstrates her embodied dialogue with a wild doe through various stages of painting an image of the doe onto plywood.Her creative process shows a participatory relationship arising between herself as researcher and the doe, her fellow subject.Throughout the painting's development, diverse subjectivities emerge, including the doe, the plywood and the author, creating a participatory collaboration and an intersubjective experience.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.078
GPT teacher head0.348
Teacher spread0.270 · 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.

Study designNot applicable
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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