Bibliographic record
Abstract
作为首个进入中国市场的比萨品牌，必胜客一路高歌猛进，到2018年，已成为中国最大的连锁西式休闲餐饮品牌。曾经西式、休闲、比萨、欢乐是必胜客鲜明的标签，但现在随着中国餐饮市场，尤其是西餐市场日渐成熟，必胜客逐渐失去了自己的特色，给人留下老派的印象。2015年后，必胜客出现业绩下滑。 2017年11月，蒯俊（Jeff Kuai）升任必胜客品牌总经理。2018年3月，曾带领肯德基中国实现业绩复兴的屈翠容就任百胜中国CEO，她提出了必胜客品牌复兴计划，决心要重点改造品牌基本面，通过重塑品牌形象、提高产品性价比、打造“新奇乐”体验等，实现品牌年轻化，让“老牌”休闲餐厅重新焕发活力。 必胜客品牌复兴计划实施过程中，业绩仍有起伏，能否带来惊喜，还需要时间的检验。现在，实现必胜客的业绩复兴成为蒯俊的首要任务。蒯俊反复思量：必胜客以新中产阶层和年轻家庭为目标客群，这个定位合理么？他们距离这个定位要求还差哪些？必胜客采取了哪些措施？这些措施有效么？未来还要做些什么？
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 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.036 |
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".