MétaCan
Menu
Back to cohort
Record W3004484484 · doi:10.1145/3374920.3374971

Swatch-bits

2020· article· en· W3004484484 on OpenAlexaff
Lee Jones, Sara Nabil, Audrey Girouard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsCarleton University
Fundersnot available
KeywordsModular designStudioIdeationContext (archaeology)Computer scienceRapid prototypingTextileHuman–computer interactionMultimediaEngineeringPsychologyOperating systemTelecommunications

Abstract

fetched live from OpenAlex

The creation of e-textile swatches is a common practice for documenting material experiments, sharing techniques with other practitioners, and for concept ideation. The Creative Interactions Lab has developed a system that turns e-textile swatches into easily connectable "bits" so that swatches can move between being an ideation tool into a prototyping tool(kit). The benefit of this approach is that experimental swatches and ideas for their use can be easily tested in context. In this studio, participants will be invited to bring their own swatches and/or prototypes, will learn how to create modular e-textile swatch-bits, and then we will spend the afternoon making prototypes and will engage in hands-on activities with the modular swatches. The goal of the studio will be to share e-textile prototyping techniques, and to discuss the potential for modular swatches to be incorporated into e-textile prototyping processes.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.100
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1000.020

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 head0.022
GPT teacher head0.226
Teacher spread0.203 · 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 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

Citations21
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicInteractive and Immersive DisplaysFrench-language works237,207