Bibliographic record
Abstract
这是一个典型的博弈论案例，主要描述星巴克面对中国日趋复杂的咖啡新零售市场与竞争对手展开对弈的过程。星巴克进驻中国的前19年，一直凭借其“第三空间”模式成为市场的领导者。然而， 2017年10月瑞幸的诞生，让星巴克受到了前所未有的威胁和挑战。瑞幸在中国市场创造性地推出了现磨咖啡的外卖和自提业务，并且疯狂烧钱补贴用户，同时快速扩张门店数，导致星巴克中国业绩在一年内损失了大约5%。 于是，2018年，星巴克也推出了外卖和“啡快”自提业务，与瑞幸展开了针锋相对的博弈。不仅如此，自2020年起，星巴克计划三年内要在中国再开1,877家门店。 为此，它应该继续坚持其“第三空间”路线，还是应发力新零售赛道？或者，它应兼顾二者？对于星巴克的战略，瑞幸会做出何种反应？
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".