Science of giants: China and India in the twentieth century
Bibliographic record
Abstract
Abstract How might one tell the histories of China and India – two countries that have come to be seen as twenty-first-century giants? How might one tell of how they look to the world and to each other? In this issue we juxtapose, connect and compare the two. Ours is an attempt at a historiography of twentieth-century modernity in China and India beyond the encouragement of Euro-American historiography. We seize this opportunity provided by the contemporary engagement and concern with the two countries to reinterpret the narratives of their twentieth-century transformation, which are far from settled at the moment. We bring historical knowledge to speak usefully to the excitement, anxiety and aspiration around science and technology in China and India. We bring the same to speak meaningfully to the cynicism, admonition and expectations that the world has of them. We use China and India as a method of exploring new historiographical questions of science. We are invested in extending the relevance of studying China and India to the world at large through connections, references and juxtaposition, and by raising questions that, on the one hand, expose the limits of the Euro-American experience and, on the other, open up the intellectual and historiographical space for narratives and theoretical frameworks that are not tied to geopolitical significance. This paper sets out these issues and introduces the papers of the collection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".