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Synchronous Computer-Aided Design (CAD): A Mid-level Technology Affordance Perspective

2022· article· en· W4286620535 on OpenAlexaff
Tucker J. Marion, Alison Olechowski, Satish Nambisan

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffordanceComputer scienceHuman–computer interactionCloud computingKnowledge managementSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Recently, cloud-based software enabling real-time collaboration has migrated into the realm of physical product design, with computer-aided design (CAD) software platforms such as PTC's Onshape and Autodesk's Fusion 360. Now that CAD is becoming cloud-based and offers synchronous design and collaboration among contributors, little is known from a theoretical and practical viewpoint on the affordances that impact synchronicity as seen through the lens of the development of physical product design. Recent research has begun to use a socio-technical affordance lens to examine innovation and knowledge creation in large-scale collaboration networks. Technology affordances are relational and denote action possibilities offered by a set of technology features to meet the goals of an individual, group, or organization. This conceptual research fills several gaps in our current theoretical understanding of collaborative CAD tools by illustrating a lack of differentiation amongst the features of the tools, and therefore lack of clarity on the complex affordances of these new tools. Secondly, there is a lack of a conceptual framework that links high-level social-technical affordances with the engineering actions undertaken in the CAD platform. Our research develops a conceptual framework for mid-level affordances of cloud-based CAD that links specific engineering challenges within knowledge-based theory on collaborative networks. We illustrate our mid-level affordances with real-world design data.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.266
Teacher spread0.235 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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