Gotta Catch Em' All: The Compelling Act of Creature Collection in Pokemon, Ni No Kuni, Shin Megami Tensei, and World of Warcraft
Bibliographic record
Abstract
Since the release of the first Pokemon video game(s) in 1996, the need to catch 'em all has captivated players around the world. While the collection of objects, coins, experience, and points has played a significant role in many main stream video games over the years, Pokemon took the concept to a whole new level by enticing players to gather a massive collection of pocket monsters, each with their own unique abilities and aesthetics. This paper attempts to answer what makes this form of collection so compelling through an investigation of four different games where the collection of trainable creatures, used to do battle on behalf of the player's main character, plays a central role: Pokemon X/Y (2013), Ni No Kuni: Wrath of the White Witch (2010), Shin Megami Tensei IV (2013), and World of Warcraft: Mists of Pandaria (2012). Four common themes surrounding creature collection are identified: Immortality, exploration, organization, and specialized knowledge. These themes are uncovered through a close reading of the four above mentioned games through the theoretical lenses of Azuma’s (2009) “Database Animals”, Greenberg et al’s (1986) Terror Management Theory, and McIntosh & Schmeichel’s (2004) social psychological perspective on collectors and collecting. The paper concludes with a discussion of McIntosh & Schmeichel’s (2004) eight steps of the collection process, and argues that the medium of the video game allows for the elimination of half of those steps, partially explaining the popularity of creature collection video games in our postmodern world.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".