Uber in China: Platform Strategy and Lean Innovation
Bibliographic record
Abstract
Since its founding in 2009, Uber Technologies Inc., relying on its disruptive business model, had in six years swept across over 300 cities in more than 60 countries worldwide. However, this ride-hailing platform aimed at optimizing “information, people and resources” had been plagued by regulatory hurdles and resistance by the taxi industry and governments in many destination countries in the process of rapid expansion. China was one of the markets that fueled Uber’s ambitions. In the wake of its entry into the Chinese market in August 2013, Uber fought a costly battle with local ride-hailing service rivals Didi Kuaidi, Yidao, and Shenzhou. To affirm its long-term commitment to China, on October 8, 2015, it officially moved into the China (Shanghai) Pilot Free-Trade Zone, establishing the Shanghai Wu Bo Information Technology Company Limited. It was the very first time that Uber had founded an independent operational entity in China aiming at becoming a real local company. However, could Uber China’s strategies enable it to take root and thrive in China?
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".