DIGITAL LABOR AND RENTIER PLATFORM CAPITALISM: REFORM OR REVOLUTION?
Bibliographic record
Abstract
Digital labor has become an umbrella term for describing a range of digitally mediated practices from paid work in the gig economy (Srnicek 2017) to cultivating a personal brand online (Scolere, Pruchniewska, and Duffy 2018). This wellspring of activities now referred to as labor has muddied the waters, making digital labor an ambiguous concept at best (Gandini 2021; Goodwin 2022). This framing of user activity as labor also has limitations, as it necessarily produces reformist, rather than revolutionary, political ends. Following Sadowski (2020), this paper challenges the conceptual framework of digital labor by re-theorizing the user/platform relation as rentier capitalism. Engels (1970) explained how tenants confront landlords not as sellers of labor-power but buyers of a commodity, and we argue that typical social media users confront platforms in an analogous way. Platforms thus only circulate existing value rather than create it, and this distinction matters in understanding their role in economic crises. Because the digital labor concept misidentifies the user/platform relationship and concedes the commoditization of communication, reformist demands emerge from this discourse, like “Wages for Facebook” (Ptak 2014 as cited in Jung 2014) or data ownership as compensation (Chakravorti 2020). Capitalist data relations (Couldry and Mejias 2020) and the profit motive of corporate platforms cannot be addressed by renumerating users. As platforms attain infrastructural status (Plantin et al. 2018), our politics must reflect the need for their transformation into public utilities with democratic accountability, a revolutionary demand that has been displaced in the turn towards digital labor.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".