Cooperative innovation subnetworks in the Chinese new energy vehicle industry: structure and coordination
Bibliographic record
Abstract
The overall synergy of technical-link-based cooperative innovation has the potential to drive industrial competitiveness, yet there is a gap in the knowledge of the cooperative innovation subnetworks based on various key technical links in a modular manufacturing industry. This study scrutinised the structure and coordination of three key technical-link-based subnetworks in the Chinese NEV industry by an empirical analysis based on joint patent data from 2009 to 2019 and a composite coordination measurement model. The findings indicate that the cooperative relationship within the three sub-networks all become closer to the obvious ‘core-periphery’ structure since 2016, with a downward trend in network density. The coordination of the three subnetworks has gradually changed from an uncoordinated degree to a low-coordinated degree, and the battery technology subnetwork has a generally lower-order degree. Some suggestions for improving the overall coordination are provided. Our study contributes to a better understanding of cooperation network dynamics with a lens of improving the balance in structural properties from technical-link-based subnetworks in a modular manufacturing industry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".