Bibliographic record
Abstract
Firms in mature industries often find themselves at a strategic crossroads, forced to choose between consolidating their position in existing market segments or breaking into new markets. At SAIC-GM-Wuling Automobile Co., Ltd. (hereinafter "Wuling"), the scales tipped in favor of the latter option: the company challenged the status quo by developing the Hongguang MINIEV (hereinafter "MINIEV"), a model featuring a minimalist design and no redundant features. The company also encouraged customization to adapt to shifting user preferences, turning the MINIEV into a fashion statement. By doing so, Wuling could avoid fighting for existing market share and break into the mass market for customized cars. In April 2021, nine months after its launch, the MINIEV was China's best-selling electric vehicle (EV) for the eighth consecutive month. It also topped the global EV sales rankings in January and April 2021. Despite its success, the MINIEV did not bring a sense of safety for Wuling's general manager Shen Yang and deputy general manager Xue Haitao. Instead, the two worried about how long the sales momentum would last, what sort of product strategy Wuling should adopt in the future, and which path to take—specifically, to tap into the higher-end segment with upgraded products or to consolidate its position in the value-for-money segment.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.030 |
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".