Synchronous Computer-Aided Design (CAD): A Mid-level Technology Affordance Perspective
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".