(Re)Creating Disney: Converging Game World Architecture in Kingdom Hearts
Bibliographic record
Abstract
The Kingdom Hearts franchise (2002-2020) is truly a product of convergence culture: in its aesthetics and narrative world, it unites games, films, animations, fairy tales, comics and cartoons. The games’ premise to merge intellectual properties from Disney and Square Enix into one coherent universe strikes as an ambitious effort with contrasting themes, motifs, characters, and worlds sharing a single stage on top of a new cast of characters and an original storyline. An analysis of any franchise is often associated with complex licensing structures, its economic impact, and the great financial endeavour to create multimedia franchises. With a franchise such as Kingdom Hearts however, its franchise relationships to other media can be made apparent through a media-centred analysis, allowing us to understand its franchise character from within. One method to make this approach possible for instance is to look at how the franchise delivers on its cross-collaboration premise by creating game worlds inspired by Disney. Some of these worlds are seemingly exact copies of their original and others deliver a new experience altogether. It is exactly this ambivalence that truly stands out in the franchise, juggling between old and new.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".