Bibliographic record
Abstract
这个(A)、(B)系列案例主要呈现盒马在零售市场如何利用大数据资源、借助新技术打破传统线上、线下边界，创造新的零售商业模式。与硅谷式的“从0到1，再从1到n”精益创业的思路不同，盒马在“1”尚未形成时，就开始了模式的复制。这个系列案例的特别之处在于，它包含两个逐层递进的90分钟教学计划，不仅可以帮助同学理解商业模式蓝图的9个模块及其之间的契合度、联动关系，还可以帮助同学理解数据和新技术能如何驱动商业模式创新。更进一步，它还能激发同学去思考，如何以战略的视角去捕捉商业模式的创新点。甚至，它还能启发同学去辩证地思考在创业时是否要遵循硅谷式的从0到1，再从1到n的创业思路及客户开发模型。 案例(A)主要描写盒马在中国零售市场两年半的创业创新故事，展现了盒马线上线下一体化商业模式的形成及演变过程。发展到2018年6月，盒马已在全国开了46家实体店，包括一家引入机器人的店、一家面向上班人群提供早午餐的F2便利店，并增加了电商业务盒马云超，以及与线下零售企业合作开设了类盒马店——“盒小马”。
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.027 |
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".