Bibliographic record
Abstract
Many millennial Animal Crossing players will experience the joy of paying off their beautiful three-floor in-game home only to have that joy cut short by the crushing realization that they may never experience homeownership in real life. Who do we then take that anger and disappointment out on? The capitalists with a stranglehold on the housing market? The governments and companies holding our lives hostage for student loan debt? Our landlords who take most of our income each month so we can keep a roof over our heads? Our bosses who are criminally underpaying us for our labour? Or is it a fictional racoon? Arguments about the ethics of Animal Crossing’s non-playable character Tom Nook are inescapable in online discussions about the Animal Crossing series. These discussions generally have two sides: either Tom Nook is a capitalistic villain who exploits the player’s labour for housing, or he is a benevolent landowner who helps the player out in hard times. Vossen first sets the stage by discussing the cultural significance of both the Animal Crossing series, focusing in on Animal Crossing: New Horizons (2020), and the millennial housing crisis. She then examines the many tweets, memes, comics, and articles that vilify Tom Nook (and a few that defend him) and asks: are we really mad at Tom, or are we mad at the cruelty and greed of the billionaires, bosses, and landowners in our real lives? Vossen argues that what she calls “Nook discourse” represents the radical social potential of Animal Crossing to facilitate large-scale real-world conversations about housing, economic precarity, class, and labour that could help change hearts and minds about the nature of wealth.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.020 |
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".