Wildcat Strike Season: The Origin and Limits of Platform Driver Protests during the COVID-19 Pandemic in Indonesia
Bibliographic record
Abstract
This article examines the widespread protest actions carried out by gig workers, especially actions using the wildcat strike, with case studies from Indonesia. During the COVID-19 pandemic (March 2020 to March 2022), a total of 47 wildcat strikes were carried out by platform drivers in Indonesia. Why were most of the protests by gig workers in Indonesia carried out through wildcat strikes? Can these wildcat strike actions win workers’ demands? Unlike the claims of several scholars that wildcat strikes tend to appear in authoritarian state labour control regimes, becoming, in these cases, an effective form of movement in winning demands, in Indonesia a despotic labour market, repressive employers’ actions, platform drivers’ distrust of existing driver organisations and the obstacles to organising can explain the emergence of wildcat strikes. Though these tend to be effective in responding quickly to specific problems at the local level, they have limitations, being unable to win their demands in national or wider contexts.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".