Do Wishes Matter? National Security and the Limits of Normative Entrepreneurship in Anti-Personnel Landmine Regime Formation
Bibliographic record
Abstract
Normative entrepreneurs, i.e., those political actors who attempt to promulgate a norm into wide acceptance, can influence the formation of international regimes once the norm they promote becomes widely accepted by most of the world's countries.However, normative entrepreneurship has limits since not all states accept the proposed norms.This paper will try to answer the question of why that is the case.According to the article, some nations refuse to adopt standards that they believe could jeopardise their national security.Other states can accept the norm even with their national security interests affected due to humanitarian and diplomatic reasons.As a result, a regime could exist and be formed by normative entrepreneurs.However, some powers, potentially the major ones, might not accept it, limiting the regime's efficacy.The article will present how this securitycentric framework functions using the case of the anti-landmine norm and the Ottawa Convention.This approach can explain the membership limits that the regimes produced by normative entrepreneurs such as the Ottawa Convention face.Furthermore, the article will outline how this framework can be used to adjust the anti-personnel landmine norm to a less demanding form, i.e., in such a manner as not to compromise national security and make the spread of the regime-forming norm possible again.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".