Bibliographic record
Abstract
China and India rank among the world's largest developing nations, fastest growing economies, most populous states, and greatest ancient civilisations. But most importantly, they are among the foremost rising powers in Asia. In the past quarter of century, both China and India's productive forces and overall national strength have been constantly enhanced, which are now widely perceived as ‘the rise of China’ 1 and ‘the emerging India’ (see, for example Cohen 2001; Ma 2002). The rise of China has to be grasped not only in terms of the past (when the Chinese people stood up, became enriched and strengthened under the first, second and third generation of leaders, respectively), but also in terms of the present and future (of a ‘peaceful rise’ under the new generations of leaders and in the wake of globalisation). Fifty years ago, Beijing and New Delhi jointly proposed ‘Five Principles of Peaceful Coexistence’ (known as Panchsheel in India) to guide bilateral relations between the two newly independent countries. However, the Sino-Indian relationship has experienced an up-and-down process since then, and the resolution of bilateral issues and the establishment of mutual trust still have a long way to go. Therefore, it is extremely necessary to reiterate and materialise Panchsheel, especially in the context of the simultaneous rise of China and India in Asia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".