Game Design for a Fiverr: Precarity, Regionality, and Platform-Mediation in the Gig Economy
Bibliographic record
Abstract
In this article, we investigate users who sell complete design services (i.e., ostensibly creating a full, original game for a client) on the gig economy platform Fiverr. By studying the platform’s affordances and analyzing user profiles, we construct two central arguments: First, we contend that gig economy platforms facilitate, shape, and moderate labor in ways that vary from more commonly discussed models of game design. Second, we push back against Fiverr’s claims of a boundaryless workforce by analyzing local conditions that concentrate labor in particular jurisdictions. After briefly reviewing the history of gig labor, we use the walkthrough method to analyze Fiverr: reviewing registration processes, protocols between buyers and sellers, and platform governance structures. We then survey fifty seller listings to determine what services are available, how much they cost, and how they are clustered geographically. Next, we address the prevalence of Pakistani users among our sample of sellers by scrutinizing global wage inequities and regional initiatives that may push workers toward the gig economy. To close, we reflect on Fiverr’s place in the game design ecosystem, investigate how gig economy labor is framed in educational institutions, and touch upon our research limitations. While gig economy platforms are often critiqued for labor exploitation or mocked for providing poor-quality services, these are both oversimplifications of complex economic, institutional, and policy assemblages. Ideally, this article will serve as a first step in better understanding game development on gig economy platforms and their power to reshape geographies of game development.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".