Platform Labor and In/Formality: Organization among Motorcycle Taxi Drivers in Bandung, Indonesia
Bibliographic record
Abstract
Abstract There is a growing consensus that emerging forms of flexibilized platform labor (e.g., Upwork, Uber) necessitate new forms of mobilization to resist exploitation, given workers’ atomization and lack of statutory rights. However, Euro‐American concerns about radical reductions in labor security are countered by workforces in the “near South,” where precarious, unprotected work has long been the norm. I explore incrementalist organization in motorcycle taxi ( ojek ) drivers’ resistance to the flexible labor regime of Go‐Jek, an Indonesian ride‐hailing app. I examine ojek pangkalan (older‐style informal‐sector drivers) and Himpunan Driver Bandung Raya (HDBR, a grassroots app‐based driver association) in the city of Bandung. Although antagonistic toward each other, ojek pangkalan and HDBR employ similar improvisatory strategies, notably micro‐territorial basecamps and grassroots social security, to establish claims to their working lives. Incrementalist strategies in Indonesia are thus highly flexible in helping workers manage precarity across formal and informal contexts. By examining organization repertoires among app‐based and older‐style ojek drivers, this paper contributes to discussions about how the precarity of platform labor is produced and managed in a global context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".