Bibliographic record
Abstract
Playwriting guides, creative writing handbooks, and screenwriting manuals are replete with guidance as to how an author should express their themes. One common rule of thumb is that the climax is where the themes, the characters, and the narrative’s result converge in the terminal of the thematic statement. If the theme is established and elaborated on during a film, then its climax is where the film’s authors express their position on the matter through the narrative’s result or lack thereof. As part of my Ph.D. artistic research, I have written and directed an interactive film, The Limits of Consent; the film follows a tree-structure where the narrative splinters at the end of the second act and presents nine separate climaxes for the film. Each climax is significantly different in either its character-focus, action, tone, style, and, crucially, its expression of the film’s themes. In this article, by examining how the thematic portfolio of The Limits of Consent was established and then elaborated on in different ways depending on selected ending, I will explore the implications of this difference between a traditional film and an interactive film and how a filmmaker may present multiple thematic statements based on the same narrative.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.084 |
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".