IN THE SHADOWS OF THE DIGITAL ECONOMY: THE GHOST WORK OF INFRASTRUCTURAL 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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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