Unfolding Laypersons Creativity Through Social VR - A case study
Bibliographic record
Abstract
Involving laypersons in collaborative design projects faces the challenge of having an adapted representational ecosystem. There is a lack of adequate representational tools for multidisciplinary actors to graphically and physically vizualize and externalize their ideas. Using VR is a promising way of renewing participatory design, but settings with VR raise the difficulty to express ideas on the model, and to support collaboration since using VR headsets eventually hinder design communication between participants wearing them. In this paper we present the a case study of one workshop involving non-designers as participants, based on collective 3D sketches using a Social VR system (without headsets), in which several users simultaneously and immersively sketch using handheld tablets, operating a 3D model as contextual background. The workshop was supported by a representational ecosystem containing: (1) Traditional freehand sketching on paper and working with pre-cut physical components used as boundary objects to represent a scaled model; and (2) immersive 3D model allowing collective life-sized visualization, 3D sketching and interaction. The paper describers the case study and provide insights about layperson's collaborative design.
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 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".