Mythological comedy through incongruity in Immortals Fenyx Rising : humor and playfulness in antiquity games
Bibliographic record
Abstract
While academic research into the reception of classical antiquity in video games is between fifteen and twenty years old at this point, and despite the age-old dictum that games are meant to be entertaining, little attention has so far gone to elements of lightheartedness, playfulness, or humor in games with ancient settings. This article performs a qualitative game analysis of Greek mythology-inspired comedy in Immortals Fenyx Rising (Ubisoft Quebec, 2020). We concentrate on the game’s depiction of the Greek gods, and, drawing on the incongruity theory of comedy, argue that the game elicits humor by offering unconventional characterizations of these characters that deconstruct and subvert the highbrow nature often ascribed to Classics, as well as by overtly criticizing the gods for their flaws and immoral deeds. Additionally, we trace articulations of mythology and comedy in ancient Greek literature, and recognize Lucian’s Dialogues of the Gods (second century CE) as a text with similar textual structures, depictions of divine characters and attitudes towards the ancient myths. This article offers a first step towards identifying playful languages of comedy and levity in antiquity games, as well as towards uncovering different modalities of mythology reception across a wider corpus of games presenting Greco-Roman mythology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".