“Drown Your Troubles in Coffee”: Place, Heterotopia, and Immersion in the Coffee Talk Series
Bibliographic record
Abstract
This article contributes to the growing body of research on space, place, and immersion in video games and offers an analysis of placeness in Coffee Talk (Toge Productions, 2020) and Coffee Talk Episode 2: Hibiscus & Butterfly (Toge Productions, 2023). Building on the work of Michel Foucault (1967/2008), this article begins by analyzing the coffee shop of this series as a heterotopia that allows the game characters and the player to find a form of comfort. Then, it examines the series in light of theories of immersion and pays particular attention to the place where the author played the two games – his bed – and also describes it as a heterotopia. This paper shows the usefulness of Foucault’s concept of heterotopia to understand placeness and coziness in video games, and the relevance of taking into account the physical space of play when we conduct a textual analysis.
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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 | 0.001 | 0.004 |
| 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 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".