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Record W4413802752 · doi:10.24908/iqurcp19074

Beach Combing - Intuition as Inquiry

2025· article· en· W4413802752 on OpenAlexaffvenue
Sophia Herrington, Emma Poley, Fiona McMillan, Maevis Chamberlain

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntuitionInstinctProcess (computing)Art methodologyAestheticsVisual artsSociologyArtComputer scienceEpistemologyContemporary artPhilosophyPerformance art

Abstract

fetched live from OpenAlex

Artistic inquiry is often the basis for creating a work of art, and is done intuitively for artists of all practices. Four artists will share their artistic process from initial inspiration, to research, to artistic creation. How artists approach intuitive art making is not taught in creative institutes, yet remains the backbone of the artistic process. In our own personal practices, we are able to cultivate an idea using inquiry as a vehicle for intuitive creation. Informing our process with research allows us to bring artistic creation into a structured academic setting. Through modes of learning embedded in our institution, intuitive processes have not been taught, they have been discovered and grown on our own. Each artist will speak to how their practice relates to intuitive inquiry process by answering the following: How do you think of artistic creation as a form of intuition? How do you apply your artistic instincts to your medium of choice? What inspired the specific piece of art? Explain your intuitive process used to create the artwork.

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)0.013
metaresearch head score (Gemma)0.021
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 score0.015
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.083
Scholarly communication0.0150.025
Open science0.0020.012
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.160
GPT teacher head0.404
Teacher spread0.244 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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