Bibliographic record
Abstract
This dissertation examines trade affordances across three different video games and one novel: The Realm Online (1995), World of Warcraft (2004), Counter-Strike: Global Offensive (2012), and Neal Stephenson's Reamde (2011).By trade affordances, I refer to those interfaces which facilitate the transition of digital items from one player to another.While the study of online economies has an established history, the broader social impact of trade affordances remains largely unexplored despite their ubiquity.I align these aforementioned video games with an increasing automation of trade practices within contemporary multi-user online games, as well as the growing relationship between online and offline economies.In order to demonstrate these connections, I provide a summary of early trade practices collected through blogs, playthroughs, developer notes, patches, and other ethnographic sources such as interviews and forum posts.After describing these trade practices, I survey key economic, ethical, political, and social theories relevant to the act of trading and consuming in online spaces.My critique is influenced by autonomist Marxist theory regarding the automation of work and the cycles of struggle central the relationship between labour and capital.This positions my dissertation in relation to other game studies scholars who have assessed the relationship between play and labour in video games, such as Nick Dyer-Witherford, Alexander Galloway, and McKenzie Wark.I contend that trade in online games is an increasingly capitalized act reflective of conditions of capital outside the games.In order to demonstrate this phenomenon, I provide close-readings of the previously mentioned video games and novel, as well as two single-player games that directly critique the relationship between trade, capital, and play.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".