A regional and historical approach to platform capitalism: The cases of Alibaba and Tencent
Bibliographic record
Abstract
This article situates China’s efforts to toughen the regulation of its tech companies since the late 2010s in the global context of Big Techs rein-in and the specific trajectory of economic development in China. Focusing on the three-phase development of Alibaba and Tencent since the late 1990s, we propose a regional and historical approach to study platform capitalism concerning how platform companies, through interacting and negotiating with shifting institutional conditions, have developed novel business models, organizational structures, and technological innovations. Not a static domination, state power co-shapes platform capitalism through a constant process of institutional improvisation and innovation, as well as interacting with private players. This geographically and historically conscious approach to platform capitalism not only contributes to a more nuanced understanding of the specificities and historicity of platform capitalism in China, but also helps to deprovincialize platform studies and extend its analytical relevance beyond the Euro-American focus or the disciplinary boundaries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".