RESISTING FRANCHISE CORPORATE CONTROL: HOW THE INTERNET ENABLES LOCAL PRACTICES IN POKÉMON PLAY
Bibliographic record
Abstract
Despite being a friendly face for children and an ambassador brand for Japan, Pokémon has always had a strong corporate side. As the Internet and technologies for hacking and communication became more available, players became more disillusioned about the friendliness of Pokémon as a franchise. The controlling nature of Game Freak along with the growing perception of lack of respect for its consumers has created a negative perception of the corporate aspect of the franchise. As a result, a global duality emerged; a corporate, franchise centered component versus a fan based, community-driven culture. In the global nature of Pokémon, where then does the local fit in? How do local Canadian players perceive, negotiate, and resist the corporate and global cultures to make their own play practices? This study examines ways in which the global aspect of Pokémon, both the corporate side and the community culture, influence the play practices of local Canadian players. Through one-on-one interviews, adult Pokémon players who have played Pokémon since the 1990s, provide insights into ways the Internet and other communication technologies have impacted the way they perceive and play Pokémon games. By grounding the nebulous idea of metaplay with 3 components, being metagaming, paratexts, and gaming capital, we can better understand contemporary digital play practices by integrating the communicative nature of the tools players use not only to play their games and expand their experience and knowledge, and how this works not only on a global, cultural level, but also the local, individual level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".