The Advocacy of Democratic Governance by India and China: Patterns of Consistency/Inconsistency between Declaratory and Operational Practices
Bibliographic record
Abstract
This article examines the patterns of consistency and inconsistency between how India and China advocate democratisation at the global and national levels. Addressing this question through a dualistic framework, we develop a detailed map of the rhetorical promotion of democratic governance by India and China through an analysis of 10 years of foreign affairs speeches, remarks, interviews and statements of political elites of both countries. The article argues that although China has not shied away from declarations on democracy domestically as well as on global governance, the contradictions between the clear and consistent push for democracy and equity at the global level and the highly contingent commitment to democracy at the national level remain highly salient. India’s deficiencies, by way of contrast, come not in the domain of legitimacy but effectiveness. India’s struggle to translate its domestic democratic credibility into more equitable representation at the global institutional level and into a stellar economic model at the domestic level exposes it to criticism in relationship to China. Yet, even with these gaps, the article concludes that India has some comparative advantages over China precisely because it can play a consistent two-level game in terms of the promotion of democracy both at global and state levels.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".