Proceedings of the 2nd ACM international workshop on Story representation, mechanism and context
Bibliographic record
Abstract
It gives us great pleasure to welcome you to the 2nd ACM International Workshop on Story Representation, Mechanism and Context (SRMC 08). This workshop succeeds the premier ACM MM SRMC workshop held in 2004 in New York. The SRMC workshop series positions stories as one of the fundamental forms we use to organize our lived experiences into patterned narratives that aspire to communicate that which is memorable and valuable. Whether to entertain, educate, illustrate or inspire, the storytelling act involves a dynamic interplay between an evolving network of authors, storytellers and audiences moving fluidly back and forth between lived lives and storied representations. Critical components in this process are the tools, machines and systems that serve as creative composition partners, agents of communicative distribution and supporters of constructive dialog. In the four years since the last workshop, critical thinking on issues related to computational story generation, representation, discovery and understanding has evolved and changed. Influenced by diverse disciplines, the emergence of novel insights and innovative ideas present us with a timely opportunity to reexamine the theory and practice of how people and machines can create, represent, share and understand stories. The underlying premise of this year's workshop is that better understanding of storytelling abilities by people and machines is necessary for the development of more compelling, participatory and sustainable multimedia systems. The call for papers attracted 14 submissions from Asia, Europe, South America, Canada and the United States. The program committee accepted 9 papers that cover a variety of topics, including collaborative storytelling applications, narrative graph models and computational story realism. In addition, the program includes 3 panel discussions on representing realism, balancing interactivity and control and facilitating multimedia story creation. We hope that these proceedings will serve as a valuable reference for multimedia researchers and developers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.016 |
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".