IN THE SHADOWS OF THE DIGITAL ECONOMY: THE GHOST WORK OF INFRASTRUCTURAL LABOR
Bibliographic record
Abstract
What does digital piecework have in common with laboring in the warehouse of a large online shopping platform? How is data cleaning related to digitization work and AI training in prisons? This panel suggests bringing these diverse ways of laboring in the digital economies together by considering these practices as infrastructural labor that takes the shape of shadow work (Illich, 1981) and ghost labor (Gray & Suri, 2019). Work and labor in modern, capitalist society imply power, authority and possibility for resistance, and these dimensions are crucial for understanding why and how infrastructures are realized and how they work. Infrastructure labor is ambiguous. It is both visible and invisible depending on the specific tasks and their inherent power relations (Leigh Star & Strauss, 1999). It includes both manual and cognitive labor. It is geared towards innovation as well as repair, maintenance and servitude. The panel aims to paint the contours of infrastructural labor at the margins of digital economies pointing towards forms of alienation and resistance that have for long been part of labor relations, but that are renegotiated in the context of emerging technologies within digital economies that need human labor to be sustained and further innovated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".