The Walking Simulator’s Generic Experiences
Bibliographic record
Abstract
This article examines walking simulators through the lens of video game genre study. Following Arsenault’s (2011) thesis which theorized genre as the “temporary crystallization of a common cultural consensus” (pp. 333–334), it maps the shared horizon of expectations of the walking simulator. The first section presents an overview of genre theory in the field of game studies. The second part assembles a corpus of five iconic walking simulators based on a discourse analysis conducted in four gaming communities: scholars, journalists, designers, and Steam users. The third portion builds on this discourse analysis to conceptualize five clusters of “generic resources” (Gregersen, 2014) that synthesize the collective understanding of the walking simulator’s generic experiences, which are then analyzed in the final segment with reference to one exemplar game of the corpus. Each analysis introduces a specific “generic effect” (Arsenault, 2011)—peacefulness, secretiveness, fatalism, everydayness, and self-reflexive distanciation—that contributes to ongoing efforts to outline the experiences of this genre. The conclusion ends witha brief discussion about the importance of transgeneric studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".