Contested Formations of Digital Game Labor
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
This article introduces a special issue critically investigating contemporary formations of digital game labor, with a focus on the political-economic forces, social inequalities, and technological dynamics mutually shaping these formations. Accounts of game industry practices have been at the forefront of efforts within media studies to document and theorize conditions and transformations of labor under digital capitalism. The study of digital game labor has tended to cluster around four areas of inquiry: below-the-line labor, the creative labor of game development, player-production, and game labor politics. Providing empirically informed portraits of diverse contexts and experiences of gamework, this issue interrogates multiple dimensions of precarious work and social exclusion within an industry whose playful self-image can make it a resistant object of labor-centered analysis. The contributors to this issue promote a research orientation that is attentive to how work in the digital game industry might be made more accessible and sustainable.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it