Bibliographic record
Abstract
In recent years, the increasing number of interactive films being released, has highlighted the need for further development of methods and criteria that can guide the earlier stages of development, such as the scriptwriting process. Following the framework of interactive storytelling as a spectrum, it is acknowledged that writing a script for an interactive narrative that involves branching path options for navigating through the story, or multiple endings, is becoming more common and presents its own challenges. In this context, this paper examines established criteria used for assessing narrative quality and examines currently available software for interactive scriptwriting, identifying their affordances and limitations. Accordingly, we present Scapegoat, a short interactive crime drama, based on the model of British homicide investigations, and with the objective to investigate in practice the application of the criteria for narrative quality, together with the processes and elements of scriptwriting that can lead to a strong engaging story. We propose an approach that can efficiently incorporate crucial information of the interaction design, it can be effectively communicated to the crew and cast and used throughout the production lifecycle of the film. We highlight the crucial role of the on-set script supervisor for ensuring the interaction design is not compromised, and continuity is retained. We also discuss recommendations for further developments, including the importance of engaging the crew and cast early in the development process, together with future work into the requirements of interactive commissioners for television and film, and the need for standarisation in the industry.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".