Bibliographic record
Abstract
Within the Animal Crossing series, players have always had the ability to collect insects and then donate them to a museum where they can then be permanently exhibited. This paper makes the argument that this collecting and exhibiting of game objects works to reflect many of the ways that videogames have begun to take up an increasingly prominent place within real world institutional exhibitions, archives, and collections. Through a conjoined lens that is equally informed by games preservation, etymology, and art history this essay works to unpack the intricacies of how the museum and collecting function with the Animal Crossing series. This examination of Animal Crossing will then be applied more broadly to two museums (the MoMA and the V&A) exhibition case studies, making the comparative argument that overtly taxonomic methods of display and archiving can work to deaden videogames’ inherently mutable vitality. By speculatively thinking of videogames as things akin to the bugs of Animal Crossing, to be kept alive throughout the archival process rather than dead objects to be preserved, a new, more productive lens of videogame curation can be gleaned.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".