Marketable religion: How game company Ubisoft commodified religion for a global audience
Bibliographic record
Abstract
Videogame companies are selling religion to an overwhelmingly secular demographic. Ubisoft, the biggest company in the world’s biggest cultural industry, created a best-selling franchise about a conflict over Biblical artefacts between Muslim Assassins and Christian Templars. Who decides to put religion into those games? How? And why? To find out, we interviewed 22 developers on the Assassin’s Creed franchise, including directors and writers. Based on those, we show that the “who” of Ubisoft is not a person but an industry: a de-personalized and codified process. How? Marketing, editorial and production teams curb creative teams into reproducing a formula: a depoliticized, universalized, and science-fictionalized “marketable religion.” Why? Because this marketable form of religious heritage can be consumed by everyone—regardless of cultural background or conviction. As such, this paper adds an empirically grounded perspective on the “who,” “why,” and “how” of cultural industries’ successful commodification of religious and cultural heritage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".