Bibliographic record
Abstract
本案例讲述了跃龙汽车CEO王捷深感公司创新乏力的困境，召集CSO、CTO和CMO召开讨论会，寻找“创新思维与方法”。王捷希望在本次讨论会上，由这三个关键角色组成创新“铁三角”，参照4幅现有客户的一天行程，寻找出具有商业价值的客户痛点及解决方案，来演练和萃取创新的方法与机制。在课堂教学中，“讨论会”将以团队工作坊形式来展开，案例授课者将引导学员学习并实践创新的方法论，体悟发散思维和收敛思维的运用，并激发他们找到更多促进创新的方法。这种思维方式的价值在于它结合了创造性思维和批判性思维的优势，促进了创新解决方案的产生，并有助于解决复杂和多变的问题。
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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.042 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".