Bibliographic record
Abstract
Abstract The distinction between everyday life and work is gradually diminishing, as productive capacities are increasingly hard-coded into quotidian activities bearing little resemblance to colloquial understandings of “work”. Digital labor research has made important contributions to our understanding of these processes and their attendant relations, inequalities, and implications. However, this body of research has insufficiently attended to the spaces through which this labor takes place. On the one hand, most research foregoes the spatial forms and relations through which the labor occurs. On the other hand, when the spaces of digital labor are considered, it is usually done through its “absolute” spaces that rely on Euclidean geometries. In this chapter, I argue that a relational spaces framework is needed to advance understanding of digital labor. A relational framework conceives of actors and practices as constituted through networks and connections, and space as produced for phenomena like digital labor. With relationality, digital labor is not confined by nation-state boundaries nor as occurring only at a simple location on the globe, but instead as constituted by intertwined positionalities that span the globe. A relational spatial framework also enables an analysis of digital labor as immaterial, cognitive, attentional, and symbolic labor, rather than as a discrete, remunerated act.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".