ZenStates: Easy-to-Understand Yet Expressive Specifications for Creative Interactive Environments
Bibliographic record
Abstract
Much progress has been made on interactive behavior development tools for expert programmers. However, little effort has been made in investigating how these tools support creative communities who typically struggle with technical development. This is the case, for instance, of media artists and composers working with interactive environments. To address this problem, we introduce ZenStates: a new specification model for creative interactive environments that combines Hierarchical Finite-States Machines, expressions, off-the-shelf components called Tasks, and a global communication system called the Blackboard. Our evaluation is three-folded: (a) implementing our model in a direct manipulation-based software interface; (b) probing ZenStates' expressive power through 90 exploratory scenarios; and (c) performing a user study to investigate the understandability of ZenStates' model. Results support ZenStates viability, its expressivity, and suggest that ZenStates is easier to understand-in terms of decision time and decision accuracy-compared to two popular alternatives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".