Bibliographic record
Abstract
Over twenty years since its original release, Final Fantasy VII (Square 1987) fans continue to debate the video game’s world and characters as they are mixed and remixed into new licensed products. This article explores the fan metanarrative that circulates the story, ludology, and industry discourses that bind Final Fantasy VII. It will demonstrate how fan practices operate within community spaces to locate, present, and police both knowledge and meanings about a fictional world that itself is continually being reshaped by the transmedia production milieu. This article explores the ongoing fan debates circulating characters Cloud, Tifa, and Aerith from Final Fantasy VII, and their respective remixing into the Kingdom Hearts franchise. Through a discourse analysis (Gee, 2007) of online Western fan bases, published above-the-line production interviews (Mayer et al. 2009), and self-reflexive experiences (Hills 2002), I seek to demonstrate the complexity of fan practices and how they attempt to locate (and generate) narrative coherency. I will argue that fans do not simply enjoy games for their variance in gameplay and story but seek a better understanding of a growing fictional world that is complex and is subject to sanctioned rewrites. Drawing on Eiji Ōtsuka’s theories on world and variation (2010), this article will demonstrate how fans can function as textual barristers in their attempts to untangle the media mix (Steinberg, 2012) of Final Fantasy VII through its ongoing reiterations, adaptations, and world-sharing with Kingdom Hearts series.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.034 |
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".