Bibliographic record
Abstract
While video games have become a popular medium for storytelling, as a more subjective form of game content, evaluation of written or generated narratives remains difficult.We propose a model for game narrative (the combination of story and its discourse) that can be used to both help write game stories, and evaluate existing ones.This model is based on scholarly models of narrative, alongside narrative structuralism, which can give information about each narrative event and its' position within the overall sequence of events making up the story.We also show from the literature that strong stories feature an external goal or quest which can only be achieved by a character resolving their own internal struggles.To create our model, we performed a study of existing video game stories, using the aggregate critical score as our metric for quality.Stories were broken down into their component events, and data was logged about each event's importance, whether it sourced internally from a character or externally, and whether the event corresponded to a component of narrative macrostructures such as "The Hero's Journey".Results from our analysis show a strong relationship between the ratio of internal "character-driven" events versus external "plot-driven" events and a story's quality.We were also able to make recommendations about overall story event structure, such as limiting strings of external, or "plot-driven", events.I would also like to thank Dr. Ali Arya, who stepped in to fill Anthony's shoes.Coming into a project in its late stages, and having no knowledge of what we had done before is a difficult situation.However, Ali worked hard to make sure the adjustment went as smoothly as possible.My thanks extend to the entire administration and faculty of the School of Information Technology, who went above and beyond to make the transition for us students as easy as they could, despite dealing with the loss of a valued colleague and friend.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".