Bibliographic record
Abstract
This paper uses the case of China’s mobile telecom industry to illustrate the challenges of pursuing national industrial policy objectives in the context of a highly dynamic and interconnected global industry. The Chinese state deployed a full arsenal of industrial policy tools in its effort to develop a Chinese modem telecom standard (TD-SCDMA) and support the development of domestic firms, yet success has been elusive. This outcome reflects the difficulty of creating a protected industry ecosystem for national firms in an industry that is increasingly dominated by global platforms. The outcome also reflects the rapid rate of change in the industry. While the state was using command-and-control methods to foster the development of core interconnect technologies, the locus of competition in the sector shifted closer to the consumer: toward handset operating systems, applications, and mobile services such as WeChat. The interconnect standards and technologies that had been the focus of China’s industrial policies are now largely generic and take up only the first few layers of handset architecture, and even handsets have become near-generic portals to on-line content, platforms and services. The Chinese firms that have thrived in this environment benefited from the protection provided by China’s Great Firewall, but are not otherwise a direct product of state support. They are entrepreneurial firms that understand the local market, and have been able to build novel solutions on top of global technology platforms. Their products and services may not be the “big” innovations that state planners often favor — services rather than semiconductors, for example — but these firms are highly competitive and have tremendous leverage within the huge Chinese domestic market.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".