MétaCan
Menu
Back to cohort
Record W2108749458 · doi:10.1109/iv.2006.84

Plot-polling: Collaborative Knowledge Visualization for Online Discussions

2006· article· en· W2108749458 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
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPollingPlot (graphics)Computer scienceVisualizationConstruct (python library)MoodOnline discussionWorld Wide WebHuman–computer interactionMultimediaData sciencePsychologyArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

Online communities are extremely popular; yet most of them still rely primarily on text for knowledge creation and communication. This paper describes a graphical Webpoll prototype - a mix of information, knowledge, and social visualization - that has been designed and deployed in an online discussion board on herbal antidepressants. The prototype, hereby named the "plot-poll", allows users to collaboratively construct a sequence of mini histograms that indicate experienced mood change during a ten week period. The pilot study shows that plot-polling is quite effective in engaging low-frequency contributors to participate. More work is required to support these findings, and reveal whether the graphic format is able to provide affective qualities to the user experience

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.351
Teacher spread0.324 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicData Visualization and AnalyticsFrench-language works237,207