Bibliographic record
Abstract
Introduction The enduring conflict between India and Pakistan may be moving towards a period of détente. The leaders of both countries met at the outset of 2004, essentially agreeing that the Kashmir conflict should be handled peacefully. This might be a cause of great optimism. One of the key sources of conflict in their relationship – Pakistan's irredentism and India's resistance – may be declining. However, at the same time, there have been repeated efforts to assassinate General Pervez Musharraf by forces that oppose moderation. The simultaneity of these two sets of events is suggestive – that there are grave domestic costs for making peace, and that Musharraf, if sincere, may be following Anwar Sadat more closely than he would like. This brings us to a key shortcoming of the enduring rivalry literature – domestic politics matters but is undertheorized. That is, domestic politics seems to do a lot of the work of causing, prolonging, and ending rivalries, but most scholars in this debate treat it in an ad hoc fashion. By focusing on the largely domestic dynamics that drive irredentism, we can get a better idea of under what conditions many rivalries will begin, worsen, and perhaps even end. Not all enduring rivalries have irredentism as a core dynamic, but many do, including the Koreas, Somalia and Ethiopia, and China and Taiwan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".