Bibliographic record
Abstract
Narratives and stories, regardless of the form of media with which they are conveyed, are found at the core of the most influential cultural works. Narrative and emotion go handin-hand, and a powerful experience is created when the two are in harmony. Video games are potent storytelling tools in that they utilize many forms of media, like visual, sound, text, and interaction-based art to transmit messages through narrative. While many factors contribute to the conveying of emotions to the audience of a work, the properties of the visual sense, and the art that makes it up, must cohere to the other properties of a work of art like a video game. As a game developer creating a work with a narrative reliant on the successful conveying of the emotion of mystery to its audience, visual coherence to these emotional expectations is critical. Art style itself is important for properly expressing emotions, and in regard to the emotion of mystery, factors like repetition, dark corners, and eerie shapes may hold the key to creating a game world which strongly expresses the emotion of mystery. This MRP attempts to analyse various methods in which the visual art of a video game can augment the game's overall sense of mystery.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".