International law and its discontents: Exploring the dark sides of international law in International Relations
Bibliographic record
Abstract
Abstract International law is generally considered to be a good thing. With important exceptions, such as Critical Legal Studies, scholarship in both International Relations (IR) and International Law (IL) reinforces this ‘nice law’ assumption and therefore overlooks or underestimates the law’s negative aspects. In contrast, this article assumes the power of international law to examine how international law can have effects that are unintended, unhelpful, or even perverse. In particular, I argue that international law distorts policy- and decision-making processes in liberal democracies by eroding personal responsibility and decreasing accountability; legal expertise and legal virtues crowd out important virtues of statecraft and prudence while shrinking our capacity for sophisticated moral and political thought; and an excessive focus on law can lead to suboptimal foreign policy outcomes. Rather than law being a bad thing per se, I examine the significant strategic and moral limits of international law. This raises the need to lower our expectations of international law, carefully examine the relationship between power and international law, and political responsibility and legal ethics, and more fully embrace our own personal responsibility. The article closes by suggesting a research programme on the dark sides of international law from various theoretical perspectives.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".