Bibliographic record
Abstract
2012年10月的一天，国内领先的酒类垂直电商——品尚红酒网的创始人张辉军先生正与几位红酒经销商讨论加盟合作。张辉军发现，因为互联网葡萄酒消费规模及增值速度有限、消费习惯等问题，葡萄酒垂直电商将会在两年内遇到增长“天花板”，品尚红酒必须找到新的增长模式。张辉军一方面在犹豫品尚红酒是否应该向更大的白酒市场扩张，一方面已开始尝试利用线下渠道拓展业务，成为一家全渠道酒商。从线上到线下的战略转变，品尚并没有成功模式可以学习，品尚应怎样克服随之而来的挑战？在经销商模式和加盟店模式之中，品尚应该选择哪种，又是否有创新的思路可以达到快速扩张的效果？
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.090 |
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".