MétaCan
Menu
Back to cohort
Record W3168878849 · doi:10.1017/9781108761970.002

Reciprocity and IHL Compliance

2019· book-chapter· en· W3168878849 on OpenAlexaff
Bryan Peeler

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReciprocity (cultural anthropology)International humanitarian lawReciprocalCompliance (psychology)Argument (complex analysis)Political scienceSection (typography)Law and economicsInternational lawLawState (computer science)SociologyPsychologyBusinessComputer scienceSocial psychologySocial sciencePhilosophyMedicine

Abstract

fetched live from OpenAlex

This chapter lays the theoretical groundwork for the argument. In the first section, I outline the legal regime governing armed conflict. The second section provides an initial definition of reciprocity and reviews two literatures explaining its importance to compliance with international legal regimes such as IHL. In section three, I outline what I am calling the “humanization of humanitarian law” thesis. This is the view that states are and can be expected to implement IHL obligations even if their adversaries do not. In section four, I present a more nuanced view of reciprocity. I demonstrate, via H. L. A. Hart’s theory of law as the union of primary and secondary rules, how states have maintained reciprocal strategies for dealing with IHL non-compliance through secondary rules. I then explain how the domestic, multi-actor setting of state decision making allows policy makers to use these secondary rules to respond to IHL non-compliance. In the last section, I examine logic of appropriateness theories found in the international relations and international law literatures that could serve as a basis for the humanization of humanitarian law thesis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.014
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.041
GPT teacher head0.244
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicGlobal Peace and Security DynamicsFrench-language works237,207