Organizing at the Digital Water Cooler: Social Media, Platform Organizing, and the Fight against Surveillance Capitalism
Bibliographic record
Abstract
This article explores how Rideshare Drivers United (RDU), a fledgling union of app-based drivers in California, works in dialectical relationship to processes of surveillance capitalism. First, the article gives a brief history of RDU's organizing strategy in the lead-up to two strikes in the spring of 2019. RDU capitalized on social media's advertising platforms, as well as on a purpose-built app called Solidarity, to bring together a disparate workforce. Next, drawing on Vincent Mosco's framework for the political economy of communication, the article describes how this strategy emerged in response to, and intervened in, the processes of commodification, spatialization, and structuration that constitute surveillance capitalism. Interviews with Los Angeles– and San Diego–area driver-organizers suggest that this use of digital tools has become a mundane feature of the contemporary labor and social life. The refusal to fetishize platforms opens space for app-based workers to challenge surveillance capitalism's logics through platform organizing.
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".