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Record W2603286930 · doi:10.1017/s0260210517000092

International law and its discontents: Exploring the dark sides of international law in International Relations

2017· article· en· W2603286930 on OpenAlexaff
Ryder McKeown

Bibliographic record

VenueReview of International Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsUniversity of Toronto
FundersUniversity of Adelaide
KeywordsInternational lawLawPolitical scienceMunicipal lawPublic lawComparative lawPhilosophy of lawPublic international lawSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.186
GPT teacher head0.397
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes1
Has abstractyes

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