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Record W2787791500 · doi:10.36510/learnland.v11i1.919

An Exploration of Artistic and Technological Symmetry

2018· article· en· W2787791500 on OpenAlexvenueno aff
Artemis Papert

Bibliographic record

VenueLEARNing Landscapes · 2018
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsCoding (social sciences)Computer scienceComputational thinkingHeading (navigation)Point (geometry)Reading (process)SoftwareVisual artsMathematics educationHuman–computer interactionArtificial intelligencePsychologySociologyArtMathematicsProgramming languageGeometryEngineeringLinguisticsPhilosophySocial science

Abstract

fetched live from OpenAlex

In this interview, biologist, artist, and shiatsu healer Artemis Papert describes how computational thinking can help people organize their thoughts in a more formal way. She discusses TurtleArt, a software that allows both children and adults to create two-dimensional static art images using geometry and coding as a medium. TurtleArt not only bridges the worlds of math and art, but is also easy to learn. She concludes by reading an excerpt from the article, “Teaching Children Thinking”— written in 1971 by her father Seymour Papert—as a still relevant starting point for where technology is heading.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.025
Scholarly communication0.0110.009
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.062
GPT teacher head0.376
Teacher spread0.314 · 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 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
Published2018
Admission routes1
Has abstractyes

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