MétaCan
Menu
Back to cohort
Record W2063177175 · doi:10.1177/1473871611433710

Exploring how and why people use visualizations in casual contexts: Modeling user goals and regulated motivations

2012· article· en· W2063177175 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

VenueInformation Visualization · 2012
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCasualArtifact (error)VisualizationComputer scienceHuman–computer interactionDuration (music)Data visualizationUsabilityData scienceCognitionArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

We present an artifact study that explores how people examine visual representations of data in non-work contexts, resulting in a proposed Promoter–Inhibitor Motivation Model of visualization use in casual contexts. We propose that user goals direct visualization use tasks, but the strength of user motivation is modified by promoting and inhibiting factors. Based on the duration and frequency of use for reported artifacts, we hypothesize that artifact use patterns depend on how promoters and inhibitors change over time, and we propose a six-stage model of artifact use. We hypothesize that the differences in how these artifacts were used ultimately reveals how promoters and inhibitors can be manipulated to promote frequent and long-duration visualization use. This model provides a cognitive framework for visualization designers and suggests new research directions.

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 categoriesScholarly communication
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 score0.953
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.029
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 head0.095
GPT teacher head0.303
Teacher spread0.208 · 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