Your Second Life? Goodwill and the Performativity of Intellectual Property in Online Digital Gaming
Bibliographic record
Abstract
This article examines the performativity of intellectual property in digital gaming environments, with a focus on Massively Multi-Player Online Games (MMOGs). The analysis centers on the creation and management of goodwill, an intangible asset of considerable value to corporations based on affective bonds between consumers, corporations, and their commodities in the marketplace. Most critical analyses of intellectual property consider neither the centrality of goodwill to corporate management of their intellectual properties in digital environments, nor the significance of the legal conditions that structure activity in such contexts. We develop a theoretical framework based on cultures of circulation involving network sociality, circuits of interactivity and the extensibility of the computer/user interface. This enables us to better understand shifting relations of power and reciprocity between corporations and consumers in digital gaming contexts, where the division between player-consumption and player-production is increasingly blurred. Technological capacities for consumers to become producers of gaming content changes the terrain upon which conflicts between corporations and consumers about intellectual property are negotiated and enhances the value of goodwill. An examination of Linden Lab’s Second Life - one of the fastest growing MMOGs and the first to affirm players’ intellectual property rights in their digital creations - provides an illustration of both the limits and possibilities afforded by goodwill as a form of emerging governance in game 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".