Suning.com: Transition from Traditional Retail to Smart Retail
Bibliographic record
Abstract
随着技术创新与消费升级，零售业正发生着深刻的变革。2016年10月阿里巴巴云栖大会上，马云首先提出了“新零售”的概念，指出在未来的10～20年，新零售将取代电子商务成为趋势。2017年3月，张近东在全国两会上提出了“智慧零售”概念，标志着苏宁正式入局，并于2017年底提出了“智慧零售大开发”战略。刘强东也于2017年7月发文提出了第四次零售革命和京东“无界零售”战略。经过几年的跑马圈地，各方战略布局已初见端倪，但成败还有待市场的考验。 苏宁以线下起家，逐步发展线上，再到“双线融合”，成为中国零售巨头之一，其发展可以说是中国零售业的缩影。伴随着业务的发展，苏宁也经历了数字化转型，积累了自身的科技实力。在新型零售时代，与天生具有互联网基因的阿里巴巴和京东的竞争中，苏宁不甘示弱，提出了“智慧零售大开发”战略，围绕着“场景互联网”与“智能供应链”，苏宁的布局层层铺开。苏宁下一步应该怎么走？首先，需要仔细复盘苏宁是如何随着时代的变化一步步走到今天的。更重要的是，认清新零售、智慧零售和无界零售的异同和本质。相比其他零售企业，苏宁又应发挥哪些自身的竞争优势？
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".