Bibliographic record
Abstract
This book has had as its subject matter India's long but unfinished journey toward becoming a major power. It has been concerned with questions such as: Has India had the aim to become a major power? Since when? How consistently has it pursued that end? Has it worked to acquire the wherewithal for that end? How consistently? What are the constraints that it has faced in the endeavor? The present chapter summarizes the various issues raised in the book, and then briefly discusses the appropriate strategies that are feasible for India to become a major power, as well as the question of adjustment of the major-power system to the phenomenon of rising powers in an era when the traditional recourse to violence is too risky to contemplate. These issues are of theoretical and empirical significance in the treatment of India's ambition for a major-power role. The theory: Realism and state behavior Major power , or more particularly great power as conventionally used, is a concept that is central to the paradigm of realism in the study of international relations, as is the related concept of the major-power system. The very centrality of power in realism makes these concepts critical to that paradigm. By virtue of the broad array of capabilities they command, major powers determine, whether in conflict or cooperation, the nature of the international system and its future development in the endeavor to advance their particular interests as regards security and welfare.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".