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Record W2178824098 · doi:10.1145/2817721.2817741

Smart Makerspace

2015· article· en· W2178824098 on OpenAlex
Jarrod Knibbe, Tovi Grossman, George Fitzmaurice

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsComputer scienceToolboxWorkspaceWorkbenchHuman–computer interactionContext (archaeology)MultimediaTask (project management)Exploratory researchWorld Wide WebVisualizationArtificial intelligenceRobotEngineering

Abstract

fetched live from OpenAlex

We present the Smart Makerspace; a context-rich, immersive instructional workspace for novice and intermediate makers. The Smart Makerspace guides makers through the completion of a DIY task, while providing detailed contextually-relevant assistance, domain knowledge, tool location, usage cues, and safety advice. Through an initial exploratory study, we investigate the challenges faced in completing maker tasks. Our observations allow us to define design goals and a design space for a connected workshop. We describe our implementation, including a digital workbench, augmented toolbox, instrumented power-tools and environmentally aware audio. We present a qualitative user study that produced encouraging results; providing features that users unanimously found useful.

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: none
Teacher disagreement score0.893
Threshold uncertainty score0.303

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 science0.0000.000
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 head0.037
GPT teacher head0.250
Teacher spread0.212 · 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

Quick stats

Citations34
Published2015
Admission routes1
Has abstractyes

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