Bibliographic record
Abstract
本书展示了“中国工商管理 案例奖”的一些获奖案例。在2018年至2022年期间举办的大赛中共有63个案例获奖，本书收录了其中13个案例。<br/>这13 个案例各有侧重。一些案例研究如何通过新技术和创新获取价值，或探讨战略、商业模式和财务业绩之间错综复杂的关系（例如，Freshippo、肯德基中国、汇纳科技、极客嘉，以及阿里巴巴与京东）；另一些分析了中国企业在进入 市场时所面临的挑战及其战略对策（美的和SHEIN）；有一些案例深入探讨了领导力、企业管理、家族企业继承、谈判和冲突管理（伊顿教育、安缇嘉、强大辣椒和奖金风波）；还有两个案例讨论了社会企业如何 好地平衡其社会目的与财务可持续性，以及公司如何将社会创新与商业创新相结合（老爸测评和阿斯利康）。这些案例来自中国和 商学院，凸显了 对中国商业和管理动态的关注。<br/>正如中国的一句谚语 “星星之火，可以燎原”，我们坚信，在大家的共同努力下，会有 多的教师认识到案例教学的价值，以及中国主题案例在 管理教育中的广泛机遇。我们致力于与大家合作，以满足日益增长的对具有 视野的多样化案例的需求。
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".