On a non-web-based multimodal interactive documentary production
Bibliographic record
Abstract
The most common rendering of interactive documentary film is through the web-based medium, which is not “tangible” or as immersive as a different form could be. The earlier making of the “I Still Remember” documentary's memory floating bubbles interactive with audience's participation using ordinary OpenGL was the first non-web-based prototype. We describe a new HCI process and the design of an associated programmer framework for making a passive documentary interactive using currently available tools and preserving the aesthetic and emotional appeal. It is done in a local space as an artistic installation. In this context, we briefly review the proof-of-concept design and implementation of a multimodal interactive system, the Illimitable Space System (ISS). It was designed to supplement digital artists' work for various interactive scenarios and applications. Its design supports non-web-based interactive documentary creation with speech and gesture based interaction (via Kinect), music visualization and green screening for interactive dance visualization, among other things in real-time. The ISS framework provides a unified generalized architecture that supports a configurable setup of installations, as in public places described in earlier work. We also compare advantages and disadvantages of the ISS's based XNA/C# realization to that of the earlier OpenGL prototype for interactive documentary production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".