Bibliographic record
Abstract
This dissertation conducts a qualitative study in a region in Canada, anonymized as “the District”, which sits outside of a major city, focusing on adult players of the main series of Pokémon games. The District encompasses different geographic areas, characterized by suburban growth in a rural context with limited urban areas, with a significant industrial sector that comes with a working class demographic. By interviewing players who began playing Pokémon at different points in time, ranging from the first generation of players in 1998 to those who played their first Pokémon games in the mid-to-late 2000s, the dissertation captures a snapshot of changing play practices that came alongside the deployment of high-speed and more accessible Internet in the mid 2000s. Since the debut of Pokémon in North America in 1998, Pokémon play is now significantly more complex owing to an intricate network of gaming practices and communication, contextualized by play practice developments over time, changes in hardware, and information communication technology (ICT) infrastructure in local communities. This dissertation argues the best means of engaging digital play today is the framework of metaplay, derived from Bateson’s theories of meta-action and meta-communication (1956), supported by the components of metagame (Donaldson, 2016), paratexts, and gaming capital (Consalvo, 2007). Through this framework, the study reveals inequities in Pokémon play in the District, demonstrating the impact of infrastructure and perceptions of play for adult players in regions like the District, while also demonstrating a desire for local play cultures and how ICTs impact the complicated relationship local players have with global play cultures and practices.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".