Ruling from the Shadows: The Nature and Functions of Informal International Rules in World Politics
Bibliographic record
Abstract
Informality is a fact of international life. Plethora of unwritten rules, unofficial processes, informal meetings, unconventional practices, and ad hoc bodies govern the international system. Interviews, memoirs, narratives, and other forms of communication by seasoned diplomats all point to the fact that decisions that have consequential impacts on international politics are taken in informal settings, and numerous studies show that informal institutions fundamentally shape domestic political life. Yet, systematic and explicit accounts of the nature and functions of informal international rules (IIRs) is rare in international relations (IR) scholarship. The oversight means that we are left with many unanswered questions regarding the manner in which informal rules exist and interact in the international system and, in turn, how they shape international political outcomes. This article addresses this gap in knowledge by outlining core characteristics of IIRs and their functions in international political life. The analysis shows that key IIRs are embedded in formal institutions. Thus, while IIRs are distinct from formal international rules and should be studied in their own right, it will be a fundamental error to approach informal rules with a binary mindset.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".