Revising the Status Quo of Revisionism, Grand Strategy, and International Order
Bibliographic record
Abstract
This dissertation examines a type of variance in state behavior pertaining to international conflict and cooperation. Rather than confining this discussion to a binary understanding of state behavior, between revisionism and status-quo seeking, it endeavors to provide a nuanced discussion of the type of grand-strategic orientations states undertake in pursuit of their interests. It poses the question, “under what circumstances do states aspire to uphold, seek to reform, or challenge international order?” In doing so, the study helps to understanding the gamut of behaviors that purportedly satisfied or revisionist states display. System-level material opportunities that are filtered by elite-preferences and beliefs about international order at the unit-level account for the type of grand strategies states will adopt. Through congruency testing, the dissertation identifies and explains order-conforming, order-reforming, order-retrenching, and order-challenging grand strategies. In this context, the dissertation addresses debates within Structural Realism on status-quo and revisionist states as well as grand strategy formation to produce an eclectic mid-range theory of state behavior. The hypotheses generated by this theoretical undertaking are tested through longitudinal, comparative case study examinations of U.S. and Chinese grand strategies in the post-Cold War period.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".