‘As you write your Odyssey…’: An empirical study of Classics students’ play interests and ergodic characterization in historical video games
Bibliographic record
Abstract
The various ways that players actually play historical games, and how history – or players’ conceptualization thereof – impacts play processes, remains understudied in the field of historical game studies. This study investigates how players make decisions in historical games, and assesses to what extent such choices are directed by historical considerations or ideas of history. Specifically, we examined this through the perspective of ergodic characterization, or players’ nontrivial involvement in the characterization of their in-game character. We conducted an active play experiment where Classics students from Belgium and the United Kingdom ( n = 10) played Assassin’s Creed Odyssey ( Ubisoft Quebec, 2018 ) for eight weeks and reported on their experiences through questionnaires, note-taking, and focus group interviews. We found that players’ characterization decisions in historical games were driven by four play interests: ludic (i.e., characterization based on the gameplay advantages that choices might bring, or to test out specific affordances of the game system), prosthetic (characterization as an extension of the player’s own personality), narrative (characterization based on the character’s narrative and background), and referential (characterization in reference to other characters, including historical ones). We also report other findings related to characterization and the role of history in historical game experiences. This article offers new insights into player experiences of historical games, contributes empirical data to existing research on characterization in games, and advocates for active play as a research method in historical game 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".