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Record W4396833655 · doi:10.1145/3613905.3644069

Traveling Arts x HCI Sketchbook: Exploring the Intersection Between Artistic Expression and Human-Computer Interaction

2024· article· en· W4396833655 on OpenAlexaff
Makayla Lewis, Miriam Sturdee, Denise Lengyel, Mauro Toselli, John Miers, Violet Owen, Josh Urban Davis, Swen E. Gaudl, Lanxi Xiao, Ernesto Priego, Kim Snooks, Laia Turmo Vidal, Eli Blevis, Nicola Privato, Patricia Piedade, Corey Ford, Nick Bryan–Kinns, Beatriz Severes, Kirsikka Kaipainen, Caroline Claisse, R. Huq, Mirjam Palosaari Eladhari, Anna Troisi, Ana O Henriques, Ar Grek, Gareth McMurchy, Ray Lc, Sara Nabil, Jacinta Jardine, R. W. Collins, Andrey Vlasov, Michele Cremaschi, Silvia Carderelli-Gronau, Claudia Núñez-Pacheco, Gisela Reyes-Cruz, Jean-Philippe Rivière

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsQueen's University
FundersFundação para a Ciência e a TecnologiaUK Research and InnovationMarcus och Amalia Wallenbergs minnesfondEngineering and Physical Sciences Research CouncilIrish Research CouncilEuropean Commission
KeywordsThe artsIntersection (aeronautics)Visual artsExpression (computer science)Variety (cybernetics)SociologyComputer scienceArtAestheticsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

When thinking of arts in HCI, one might be tempted to keep one’s eyes focused on prominent realms such as sketching for UX Design and design probes from participants. A closer look shows that practices go beyond this, involving a variety of arts-based expressions by researchers, the researched and third parties, e.g. graphic facilitators. Inspired by Toselli’s Sketchnote Army Travelling Sketchbook, researchers and artists contributed to a ’Travelling Sketchbook for Arts in HCI’, showcasing their arts-based practice in HCI. The resulting sketchbook explores the intersection between HCI and artistic expression, illuminating what it means to use art in HCI. It shows the breadth of Arts in HCI, illustrating the many fruitful possibilities for extending existing research and dissemination methods in HCI. It also calls into question current practices, which often do not recognise the significance of artist attribution, and, in turn, advocates for equal authorship between principal researchers and contributing artists.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.321
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2024
Admission routes1
Has abstractyes

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