Bibliographic record
Abstract
案例教学与中欧 工商学院的使命——“培养兼具中国深度和 广度、积极承担社会责任的 ”——有着天然的一致性。30年来，中欧案例中心重点开发1 500多篇反映中国商业情境、总结中国环境下商业实践和企业文化的案例。值此中欧成立30周年之际，本书编选了30篇各个时期比较有代表性的中欧案例。这些案例各有特色，涵盖了企业管理实践中的方方面面，既有偏重发展战略的，也有偏重创业创新的；既有聚焦产品营销的，也有关注数字化建设的；既有观察女性领导力的，也有讨论企业社会责任的；既有汽车、机械等制造行业，也有餐饮、零售等服务行业……可以说，这些案例折射出这30年来中国企业的方方面面，既可以作为商学院课堂的教学材料，也可以供关心中国管理领域和企业发展的广大普通读者参考。
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".