India 2023: Tactical wins and strategic setbacks in foreign policy?
Bibliographic record
Abstract
India had a busy and increasingly tumultuous year in foreign policy. New Delhi played host to both the Group of 20 and the Shanghai Cooperation Organisation, providing opportunities to shape agendas in multiple areas of global governance and international security. It used both presidencies to showcase the achievements of the Modi government, to demonstrate India’s «convening power», and, at times, to frustrate others, especially China. But, during 2023, India also attracted global attention for other reasons. In the middle of year, the killing of a Sikh separatist in Canada led some to conclude that New Delhi was running a covert programme of targeted assassinations. That incident led to a major diplomatic dispute with Ottawa and was followed by evidence, uncovered by United States authorities, of an unsuccessful plot to assassinate another Sikh separatist. These events cast a shadow over India’s strategic partnership with Washington. Towards the end of year, the outbreak of violence in Gaza brought the Modi government’s Middle East policy – especially the strong relationship forged with Israel – under greater scrutiny. Both could prefigure strategic setbacks for India, this article argues.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".