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Record W3200908759 · doi:10.1007/978-3-030-86062-2_34

The Science of Seeing Science: Examining the Visuality Hypothesis

2021· book-chapter· en· W3200908759 on OpenAlexaff
Lisa A. Best, Claire Goggin

Bibliographic record

VenueLecture notes in computer science · 2021
Typebook-chapter
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSt. Thomas UniversityUniversity of New Brunswick
Fundersnot available
KeywordsDisciplineSpace (punctuation)Computer scienceImperfectVisualizationData scienceSociologySocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Fundamental disciplinary differences may be traceable to the use of visual representations, with researchers in the physical and life sciences relying more heavily on visuality. Our goal was to examine how inscriptions are used by scientists in different disciplines. We analyzed 2,467 articles from journals in biology, criminology and criminal justice, gerontology, library and information science, medicine, psychology, and sociology. Proportion of page space dedicated to graphs, tables, and non-graph illustrations was calculated. A Visuality Index was defined as the proportion of page space dedicated to visual depictions of data and non-data information. An ANOVA indicated a statistically significant difference between disciplines, interaction between inscription type and discipline, with articles published in biology journals dedicating more page space to graphs. The significant overlap in inscription use and visuality indicates imperfect disciplinary demarcation, suggesting similar methodological and data analytic practices within a discipline and between subdisciplines.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0030.012
Scholarly communication0.0030.001
Open science0.0120.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.306
Teacher spread0.255 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2021
Admission routes1
Has abstractyes

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