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Lighting: Its Influence on Drawing Strategies

2015· article· en· W2177100525 on OpenAlexaboutno aff
Mathew Reichertz, Bryan Maycock, Raymond M. Klein

Bibliographic record

VenueVisual Arts Research · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPeriod (music)PsychologyPremiseNova scotiaVisual artsObservational studyMathematics educationArtHistoryAestheticsLinguisticsMedicine

Abstract

fetched live from OpenAlex

Abstract The illumination of a scene influences how that scene is scanned and how it is depicted. This premise, together with assumptions regarding implications for teaching observational drawing, was the basis for a pilot study in the Nova Scotia College of Art and Design (NSCAD) Drawing Laboratory. The pilot provided evidence that helped refine the question and methods described in this expanded study. As in the pilot, participants worked from common stimuli that were lit in two distinct ways. The participants drew for a predetermined period of time while their hand movements were recorded digitally and the entire process was observed firsthand. Over a period of 7 days, five participants each completed four drawings. The 20 drawings were compared and the recordings analyzed. Digital analysis generated the most informative data in that, while light’s influence on drawing strategies proved to be less significant than anticipated, changes in drawing behavior were observed with implications for teaching and learning.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.342
GPT teacher head0.467
Teacher spread0.125 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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