Bibliographic record
Abstract
33.8%的同比销量年增长率，让英菲尼迪在2015年的中国市场终于能足够引人注目。这个在2015年中国市场上唯一增速超过30%的品牌，2013年时还只是一个默默无闻的小众品牌。促使这一切发生改变的，是以英菲尼迪中国区总经理戴雷为首的中国团队，戴雷于2013年5月加入英菲尼迪。英菲尼迪希望在2017年比肩豪华车市场的德系三强（奥迪、奔驰、宝马），中国市场被赋予了主要的赶超使命，届时需要贡献至少10万辆销量。戴雷上任之前的2012年，英菲尼迪在华只有1.6万辆年销量。要在５年内实现年均44%的销量增长并且要获得跟德系三强一样的品牌力，戴雷有多大把握？特别的，进入2015年之后，英菲尼迪也像其它豪华车品牌对手们一样置身于持续低迷的中国市场。案例A描绘了英菲尼迪在中国市场2015年之前的发展轨迹，包括英菲尼迪总部对中国市场定位的变化、戴雷加入之后在品牌本地化定位、品牌营销方面的动作。案例B则陈述了面对2015年中国市场的低迷，英菲尼迪改变了之前以品牌打造为主的策略，将资源重心放在产品营销上，追求短期见效的结果。在这样一个不断变化的市场上采取不断变化的市场营销策略，戴雷能如愿实现英菲尼迪的5年规划吗？
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.039 |
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".