Corporate Ideology in World of Warcraft
Bibliographic record
Abstract
Hours have gone to blade: days, weeks of my life. To be precise, in the past year I have spent eighteen days, two hours, and seventeen minutes in Azeroth1. And my level fifty-seven hunter, Ulcharmin, is one of lesser lights in our guild, the Truants, an active group of academics, a full contingent of Ph.D.s and advanced graduate students who dedicate a significant portion of their lives to the study of MMORPGs. By my estimation, about 5 % of my total life during the past year has gone into the World of Warcraft, perhaps 7.5 % of my waking life. While I’m no more addicted to Warcraft than I am to scotch, chocolate, or sex, I’ve spent more time killing trolls over the past year than I have drinking alcohol, eating candy, or making love (combined). During this same year, I watched the first two seasons of the narrative-rich, multi-sequential TV series Lost on DVD, forty-eight episodes, all in a row. That took about thirty-five hours total, about 8 % of the amount of time I spent playing World of Warcraft. During the course of my life, I’ve read James Joyce’s Ulysses three times, twice carefully. I estimate that took about six days of twenty-four-hour time, about eighteen full eight-hour working days, about 33 % of the time I’ve spent playing World of Warcraft. Is the world that the team of developers at Blizzard have created twelve times more compelling
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".