GUERILLA CAPITALISM AND THE PLATFORM ECONOMY: GOVERNING UBER IN CHINA,TAIWAN, AND HONG KONG
Bibliographic record
Abstract
Platform firms, such as Uber and Airbnb, are emerging economic actors that aim at re-organizing the economic sectors they enter through challenging existing regulatory frameworks politically. While most studies focus on how platform firms’ political playbooks operate in European and North American democratic contexts, we know less about the regulatory and contestatory stories in non-Western and non-democratic contexts. This study aims to fill this lacuna by examining the governance of Uber in China, Hong Kong, and Taiwan. The three diverse political systems provide a comparative basis to how Uber's political playbook works in authoritarian (China), semi-authoritarian (Hong Kong), and democratic (Taiwan) systems, respectively. We introduce the concept of “guerilla capitalism” to describe how platform firms attempt to make a profit through exploiting legal grey zones or openly violating established laws. We present a critical discourse analysis of Uber’s public marketing materials, news coverage about Uber, and government reports about ride-hailing in the three cases. Our analysis illustrates (1) the convergent discursive and political strategies Uber employed to legitimize its business to change the law and (2) the divergent and contextual factors that lead to different regulatory outcomes. We argue that Uber’s operative logic lies at the swift accumulation of a large number of politically mobilizable customers and the formation of political coalitions with their customers; however, governmental responses to Uber's political playbook vary with regulatory contexts. Such an operative logic may re-shape power-relations in different political trajectories. This study affords significant opportunities for thinking about the comparative politics of platformization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".