Bibliographic record
Abstract
Players in competitive games do not always pursue efficient victory. This essay is concerned with alternative goals in competitive videogaming. Here I examine practices of play, spectation, and casting in the Age of Empires II (Ensemble Studios, 1999) community, where playing “for the legend” is a form of heroic play that differs from playing for the win. Building on Celia Pearce’s (2009) ethnographic study of play communities, Will Wright’s (2004) notion of “possibility space,” Roger Caillois’s (1958/2001) theory of forms of play, and Roland Barthes's (1957/1972) semiology of myth, I argue in favour of a design philosophy supporting play for the legend as distinct—if potentially complementary—to both (1) the meritocratic agonism of esports and (2) attempts at capturing social life within game mechanics. Age of Empires II derives value from its function as a technology supporting a friendly community beyond what is encoded in software. The game’s success is not determined only by developer design but rather depends upon the work of a community defining its own ideals about what makes a good game and a heroic player.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".