History, design and archaeology: The reception of Julius Caesar and the representation of gender and agency in Assassin’s Creed Origins
Bibliographic record
Abstract
In 2017,s Ubisoft Montreal launched the game, Assassin’s Creed Origins , with its historical background placed in Egypt by the time of the arrival of Julius Caesar. It is focusing on his involvement in Cleopatra and Ptolemy XIII’s struggle for the throne, with the plot culminating with his assassination in 44 BCE. The main goal of this article is to make an in-depth analysis of the reception of Julius Caesar in Assassin’s Creed Origins – a venture that, as is demonstrated throughout the article, necessarily passes through an examination of Caesar’s in-game relations and attitudes towards the other historical and fictional characters around him, especially Cleopatra, Bayek and Aya. The analysis of the relationships between Caesar and these other characters reveals several decisions of the game developers that can be better understood through a gender-based examination. It is proposed here that there is in the game a clear-cut representation of men and women’s activities and roles that are, in large part, structured the way they are due to current discourses and issues concerning female agency in the contemporary world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".