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Record W3155832955 · doi:10.1145/3449132

StreamSketch

2021· article· en· W3155832955 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

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffordanceInteractivityComputer scienceMultimediaLive streamingModality (human–computer interaction)Key (lock)ModalitiesHuman–computer interactionProcess (computing)Space (punctuation)Formative assessmentWorld Wide WebPsychologySociology

Abstract

fetched live from OpenAlex

Creative live streams, where artists or designers demonstrate their creative process, have emerged as a unique and popular genre of live streams due to the real-time interactivity they afford. However, streamer-viewer interactions on most live streaming platforms only enable users to utilize text and emojis to communicate, which limits what viewers can convey and share in real time. To investigate the design space of potential visual and non-textual modalities within creative live streams, we first analyzed existing Twitch extensions and conducted a formative study with streamers who share creative activities to uncover key challenges that these streamers face. We then designed and implemented a prototype system, StreamSketch, which enables viewers and streamers to interact during live streams using multiple modalities, including freeform sketches and text. The prototype was evaluated by two professional artist streamers and their viewers during six streaming sessions. Overall, streamers and viewers found that StreamSketch provided increased engagement and new affordances compared to the traditional text-only modality, and highlighted how efficiency, moderation, and tool integration were continued challenges.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.251

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 communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.356
Teacher spread0.296 · 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