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Record W6969441286 · doi:10.5683/sp3/kdzusj

Visual competencies lab 2021

2024· dataset· en· W6969441286 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVisual methodsData collectionVisual researchWork (physics)Visualization

Abstract

fetched live from OpenAlex

In the Fall of 2021, STEM researchers were invited to participate in a series of workshops delivered at the University of Guelph, where they examined a work of abstract art drawn from the SOFAM Print Study Collection (PSC). The goal of these workshops was to provide participants with skills to help them become more conscious of the mutable and subjective qualities of their looking, to provide tools to better identify and record visual findings, and to determine if the interpretive methodologies implicated in the visual analysis of art is helpful to researchers who use visual observation as a primary method of collecting data.

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)0.002
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.091

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 head0.019
GPT teacher head0.298
Teacher spread0.280 · 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
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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