Pluralism in IR Theory: An Eclectic Study of Diplomatic Apologies and Regrets
Bibliographic record
Abstract
This paper sketches a pragmatic analysis of diplomatic apologies and regrets. To conduct such a study, some points of Sil and Katzenstein’s “analytic eclecticism” should be clarified. Adapting Railton’s concept of “ideal explanatory text”, the recent works by Forland, Van Bouwel and Weber help clarifying how various causal mechanisms may be complementary to the understanding of one specific problem: different frameworks of analysis are needed to answer different why-questions encompassed in this problem. Their works parallel Kurki’s advocacy of a post-Humean conception of causation. These analyses will be presented and briefly applied to the study of diplomatic apologies and regrets, showing how liberalism, constructivism, game theory (and other theories) are complementary to understand such complex phenomena. The main purpose of this paper is to show why it is interesting to integrate Sil and Katzenstein analyses with Kurki, Forland, Van Bouwel and Weber’s to conduct a problem-driven and complexity-sensitive research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".