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Record W2018251926 · doi:10.1145/2598153.2598170

Paper vs. tablets

2014· article· en· W2018251926 on OpenAlexafffund
Jonathan Haber, Miguel A. Nacenta, Sheelagh Carpendale

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Calgary
FundersNetworks of Centres of Excellence of CanadaNatural Sciences and Engineering Research Council of CanadaSingapore-MIT Alliance for Research and Technology CentreCanada Foundation for InnovationAlberta Innovates - Technology Futures
KeywordsComputer scienceMultimediaGraphicsHuman–computer interactionGazeGroup (periodic table)Affect (linguistics)Computer graphics (images)Artificial intelligencePsychology

Abstract

fetched live from OpenAlex

With new computer technologies portable devices are rapidly approaching the dimensions and characteristics of traditional pen and paper-based tools. Text and graphic documents are now commonly viewed using small tablet computers. We conducted a study with small groups of participants to better understand how paper-based text and graphics are used by small collaborative groups as compared to how these groups make use of documents presented on a digital tablet with digital styluses. Our results indicate that digital tools, as compared to paper tools, can affect the levels of verbal communication and participant gaze engagement with other group members. Additionally, we observed how participants spatially arranged paper-based and digital tools during collaborative group activities, how often they switched from digital to paper, and how they still prefer paper overall.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0090.005
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0920.014

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2014
Admission routes2
Has abstractyes

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