Bibliographic record
Abstract
丰收蟹庄的创始人兼CEO傅骏先生在美食界、艺术界和商业界有着广泛的人脉资源。借助这些资源，傅骏于2002年设计出礼券模式为传统的大闸蟹行业带来了新鲜元素。丰收蟹庄以销售鲜活的大闸蟹为主，并开发了秃黄油、干贝蟹肉和醉蟹等深受用户喜爱的产品，其销售业绩保持11年稳定增长。然而，礼券模式的成功却受到了国家提出的“八项规定”的影响。2013年丰收蟹庄京沪两地的礼券销售收入与去年相比降低了约30%。在这样的背景下，傅骏注意到生鲜电商开始崛起，开始思索：是否应该运用互联网思维来帮助企业渡过难关？全电商模式下，丰收蟹庄采用怎样的营销策略，并如何赢得更广大用户的信赖呢？通过讨论丰收蟹庄从线下企业向电商企业转型过程中所面临的挑战及其应对策略，学员可以深入理解创始人的性格和所拥有的资源对其创业活动的影响，并进一步掌握精益创业方法论的核心思想。
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.029 |
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".