Making Science Fiction Real: Neoliberalism, Real-Life and Esports in Eve Online
Bibliographic record
Abstract
In this paper, we argue that EVE Online is a fruitful site for exploring how the representational and political-economic elements of science fiction intersect to exert a sociocultural and political-economic force on the shape and nature of the future-present. EVE has been oft heralded for its economic and sociocultural complexity, and for employing a free market ethos and ethics in its game world. However, we by contrast seek not to consider how EVE reflects our contemporary world, but rather how our contemporary neoliberal milieu reflects EVE. We explore how EVE works to make its world of neoliberal markets and borderline anarcho-capitalism manifest through the political economic and sociocultural assemblages mobilized beyond the game. We explore the deep intertwining of behaviors of players both within and outside of the game, demonstrating that EVE promotes neoliberal activity in its players, encourages these behaviors outside the game, and that players who have found success in the real world of neoliberal capitalism are those best-positioned for success in the time-demanding and resource-demanding world of EVE. This thereby sets up a reciprocal ideological determination between the real and virtual worlds of EVE players, whereby each reinforces the other. We lastly consider the “Alliance Tournament” event, which romanticizes conflict and competition, and argue that it serves as a crucial site for deploying a further set of similar rhetorical resources. The paper therefore offers an understanding of the sociocultural and political-economic pressure exerted on the “physical” world by the intersection of EVE’s representational and material elements, and what these show us about the real-world ideological power of science fictional worlds.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".