TEACHING: French Danone and Chinese Wahaha: Yet Another Example of an Unsuccessful International Joint Venture
Bibliographic record
Abstract
During a five-day visit to Taiwan on November 26, 2009, Zong Qinghou, Managing Director of the Chinese firm Wahaha, was impressed by the quality of Guangquan Corporation’s dairy products. An opportunity for another international joint venture (IJV) arose in Zong’s mind. It was too attractive to ignore in that it might give Wahaha a competitive edge in establishing a quality image after the scandal involving contaminated milk formulas by the Sanlu Group in 2008 had greatly shaken consumers’ faith in Chinese brands. However, the bitter dispute with the French multinational enterprise Danone, Wahaha’s first IJV partner, caused Zong to reconsider whether to take advantage of this opportunity. Danone finally pulled out of the IJV on September 30, 2009 after years of fighting, ending the almost 12 year relationship with Wahaha. Because this relationship had not worked out, Zong was thinking about what went wrong during the previously unsuccessful IJV. He was deep in thought (Lucy, 2009).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".