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Record W1973331852 · doi:10.1145/2702613.2732778

Exploring the Effect of Word-Scale Visualizations on Reading Behavior

2015· preprint· en· W1973331852 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Calgary
FundersAgence Nationale de la Recherche
KeywordsMemorizationComputer scienceReading (process)Affect (linguistics)SentenceTask (project management)Natural language processingWord (group theory)VisualizationScale (ratio)Artificial intelligenceHuman–computer interactionCognitive psychologyLinguisticsPsychologyCommunication

Abstract

fetched live from OpenAlex

We studied how the integration of small visualizations (word-scale visualizations) into a sentence affects reading speed and memorization during a brief reading task. In particular, we were interested in how different placement types with their inherent text appearance and layout changes affect readers. We designed a quantitative study in which we gave sentences with or without visualizations for participants to read. Then, we invited them to answer questions on the sentences. We found that the information encoded in the visualizations is more prominent and easily remembered than information in the written text, but that different placement options had little to no effect on reading performance, even if participants had different preferences.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.130
GPT teacher head0.370
Teacher spread0.240 · 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

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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