Ratava's line: Emergent learning and design using collaborative virtual worlds
Bibliographic record
Abstract
Ratava's Line is an online, 3D virtual world fashion and interactive\nnarrative project created collaboratively by students at both the\nFashion Institute of Technology (FIT) in New York City and at\nInteractive Arts at Simon Fraser University (SFU) in Vancouver,\nCanada, using emergent, collaborative 2D and 3D systems. This\ndistance learning project, developed over two months and\nculminating in an online event in multiple, remote locations,\nintegrated three key design elements: the translation of original 2D\nfashion designs from FIT students into 3D avatar space; exhibits of\nartwork of student and professional artists from New York City and\nVancouver in virtual galleries; and creation of an interactive\nnarrative "fashion cyber-mystery" for online users to participate in\nand solve in a culminating, cyber-physical event. The overall\nproject goal was to explore how online collaboration systems and\nvirtual environments can be used practically for distance learning,\nfashion and virtual worlds design, development of new marketing\ntools including virtual portfolios, and creation of cross cultural\nonline/physical events. The result of this process was an\ninterdisciplinary, cross-institutional, international effort in\ncollaborative design in virtual environments, and a successful\nexercise in emergent, collaborative distance learning. © ACM, 2004. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in International Conference on Computer Graphics and Interactive Techniques, page 25. (2004). http://doi.acm.org/10.1145/1186107.1186136
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".