Game Design for a Fiverr: Precarity, Regionality, and Platform-Mediation in the Gig Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it