Bibliographic record
Abstract
This case deals with a Chinese startup, Testin, forging partnerships with multiple large multinational corporations (“MNCs” in short, e.g., Microsoft, IBM, ARM, Intel). In order to help developers economize the time spent on quality assurance (QA), Testin was founded by Jun Wang with six of his former colleagues working at Pica Corporation in June 2011. In the past years, Testin had served over 800,000 app developers by conducting more than 150 million quality and security tests on over 2.5 million mobile apps through its automated Cloud Testin (云测) service supported by its testing labs and its Crowd Testin service for solving further problems by part-time qualified testers. It had received several rounds of financing totaling over USD 80 million. Many Chinese Internet companies tried to acquire Testin, and a well-known multinational company asked Testin to sign an exclusive service contract. Wang and his partners resisted such offers and were determined that Testin should maintain neutrality. Thinking about the five-year old enterprise, Wang wondered how to ensure Testin could “stay hungry and stay foolish”. A striking feature of these relationships discussed in this case is that the venture is based in an emerging market context (China) whereas several of the key large corporate partners are headquartered in advanced markets. Thus the partnerships being forged are arguably between non-traditional allies.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".