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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".