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Record W642285587 · doi:10.1515/2151-7509.1054

Comprehension of Graphs and Tables Depend on the Task: Empirical Evidence from Two Web-Based Studies

2012· article· en· W642285587 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStatistics Politics and Policy · 2012
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Waterloo
FundersNational Science Foundation
KeywordsComprehensionBar chartComputer scienceTask (project management)Pie chartTable (database)Sample (material)Information retrievalBar (unit)Program comprehensionNatural language processingStatisticsData miningSoftwareMathematicsProgramming languageEngineering

Abstract

fetched live from OpenAlex

Graphs and tables are an effective means of communication. However, relatively little experimental work exists examining differences between various formats in how well people understand provided information. We conducted two web-based experiments with a large, diverse sample to explore the effects of display format on respondents ’ comprehension. We found that comprehension depended on task. Graphs were better for estimating differences; however, tables were better when estimating equality and sums. We found 3D display formats reduced comprehension of pie charts but not of bar charts. Although pie charts never assisted comprehension, they often did not significantly impair comprehension either. Comprehension based on a 3-way table was as good as that for clustered bar charts but was worse for divided bar charts. Information can be conveyed graphically even with 3-way tables, but the choice of display format needs to be sensitive to the task at hand.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.179
GPT teacher head0.464
Teacher spread0.286 · 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