Hidden figures: how legal experts influence the design of international institutions
Bibliographic record
Abstract
Whose preferences influence the design of international institutions? Scholarship on the legalization of international politics and creation of international legal institutions largely adopts a state-centric perspective. Existing accounts, however, fail to recognize how states often delegate authority over institutional design tasks to independent legal experts whose preferences may diverge from those of states. We develop a principal-agent (PA) framework for theorizing relations between states (collective principals) and legal actors (agents) in the design process, and for explaining how legal actors influence the design of international institutions. The legal dimensions of the PA relationship increase the likelihood of preference divergence between the collective principal and the agent, but also create conditions that enable the agent to opportunistically advance its own design preferences. We argue that the more information on states' preferences the agent has, the more effectively it can exploit its legal expertise to strategically select and justify design choices that maximize its own preferences and the likelihood of states' acceptance. Our analysis of two cases of delegated institutional design concerning international criminal law at the United Nations and the African Union supports our theoretical expectations. Extensive archival and interview data elucidate how agents' variable information on states' preferences affects their ability to effectively advance their design preferences. Our theory reveals how independent legal experts with delegated authority over design tasks influence institutional design processes and outcomes, which has practical and normative implications for the legalization of international politics.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".