Power in practice: Negotiating the international intervention in Libya
Bibliographic record
Abstract
How does power work in practice? Much of the ‘stuff’ that state agents and other international actors do, on an everyday basis, remains impenetrable to existing International Relations theory. This is unfortunate, as the everyday performance of international practices actually helps shape world policy outcomes. In this article, we develop a framework to grasp the concrete workings of power in international politics. The notion of ‘emergent power’ bridges two different understandings of power: as capability or relation. Emergent power refers to the generation and deployment of endogenous resources — social skills and competences — generated in particular practices. The framework is illustrated with an in-depth analysis of the multilateral diplomatic process that led to the 2011 international intervention in Libya. Through a detailed account of the negotiations at the United Nations, North Atlantic Treaty Organization, and the European Union, the article demonstrates how, in practice, state representatives translate their skills into actual influence and generate a power politics that eschews structural analysis. We argue that seemingly trivial struggles over diplomatic competence within these three multilateral organizations played a crucial role in the intervention in Libya. A focus on practice resituates existing approaches to power and influence in International Relations, demonstrating that, in practice, power also emerges locally from social contexts.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.062 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| 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".