China's pursuits of indigenous innovations in information technology developments: hopes, follies and uncertainties
Bibliographic record
Abstract
Although the Chinese state has allowed US-based transnational corporations to play an instrumental role in China's information technology (IT)-led development strategy, a dominant segment of Chinese political and technological leadership has always been wary of the negative political, economic and cultural implications of continued American domination in digital technological developments. Chinese efforts at asserting greater control over a rapidly evolving networked communication infrastructure have been multifaceted. Significantly, the past few years have seen an escalation of domestic discourses on “network sovereignty” and “indigenous innovations” or the mastery of core technologies at the industrial development strategy and technological policy levels. An elite consensus has crystallized around the mobilization of national resources to catch up with the United States in hardware and software IT developments, particularly to achieve potential leadership in next-generation network technologies. However, China's quest for technological leadership in the network age continues to be constrained by a range of domestic political economic forces on one hand, and mediated by the paradoxical dynamics of interstate cooperation and rivalry in the political economy of global communication on the other. The growing transnational nature of the capitalist accumulation process, of which China's deepened global integration through its rapidly expanding and increasingly market-driven information industry has been a critical component, has further complicated these endeavors.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".