Close Reading Oblivion: Character Believability and Intelligent Personalization in Games
Bibliographic record
Abstract
This paper investigates issues of character believability and intelligent personalization through a reading of the Elder Scrolls: Oblivion. Oblivion’s opening sequence simultaneously trains players in the function of the game, and allows them to customize their character class through the choices and actions they take. Oblivion makes an ambitious attempt at intelligent personalization in the character creation process. Its strategy is to track early gameplay decisions and “stereotype ” players into one of 21 possible classes. This approach has two advantages over a less adaptive system. First, it supports the illusion of the gameworld as a real world by embedding the process of character creation within a narrativised gameplay context. Second, the intelligent recommendation system responds to the player’s desire to believe that the game “knows ” something about her personality. This leads the players to conceptualize the system as an entity with autonomous, human-like knowledge. This paper considers ways in which Oblivion both succeeds and fails at mapping player behaviour to appropriate class assignments. It does so through the analysis of multiple replayings of the opening sequence, and the application of two theoretical lenses- character believability and intelligent personalization. The paper documents moments where the dialogue between player and game breaks down, and argues for alternative techniques to customize the play experience within the desires of the player.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".