Bibliographic record
Abstract
Interactive films are defined here as an audiovisual narrative flow aimed at creating drama along with its characterizing suspense, curiosity, and surprise. Interaction is defined as an audiovisual alignment that allows the interactor to intervene or steer the story progression in different ways (by voice recognition or touch screen options). Interaction forms an added layer comparable to the addition of sound to silent movies. As with the addition of a soundtrack, an added interactive layer to the sound and image layers creates new narrative forms and audiovisual compositions. These new narrative forms include the new data-base narrative form discussed by Lev Manovich, often expressed through new digital enabled audiovisual compositions such as morphing. However, adding an interactive layer poses a series of challenges which concern the need to script, direct, edit and design a coherent work when the story, the characters and the drama may not engender immersion due to out of fiction interactive actions. Hence, story multi-bifurcation upon interaction impedes the cognitive processing of the story logic. Likewise, interactive actions may obstruct the narrative flow, thereby leading to viewser split attention and miscomprehension. This study suggests a solution to these common problems through the drama-driven model of interaction implemented in the interactive movie Turbulence.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".