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Record W4225142605 · doi:10.1145/3491101.3503723

Splash! Identifying the Grand Challenges for WaterHCI

2022· article· en· W4225142605 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

VenueCHI Conference on Human Factors in Computing Systems Extended Abstracts · 2022
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersAustralian Research Council
KeywordsSplashComputer scienceGrand ChallengesWork (physics)Human–computer interactionData scienceEngineering

Abstract

fetched live from OpenAlex

Bodies of water can be a hostile environment for both humans and technology, yet they are increasingly becoming sources, sites and media of interaction across a range of academic and practical disciplines. Despite the increasing number of interactive systems that can be used in-, on-, and underwater, there does not seem to be a coherent approach or understanding of how HCI can or should engage with water. This workshop will explicitly address the challenges of designing interactive aquatic systems with the aim of articulating the grand challenges faced by WaterHCI. We will first map user experiences around water based on participants’ personal experiences with water and interactive technology. Building on those experiences, we then discuss specific challenges when designing interactive aquatic experiences. This includes considerations such as safety, accessibility, the environment and well-being. In doing so, participants will help shape future work in WaterHCI.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.165
GPT teacher head0.351
Teacher spread0.186 · 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