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Record W4285388102 · doi:10.1093/jnlids/idac016

Reconsidering International Compensation in Historical Context

2022· article· en· W4285388102 on OpenAlexafffund
Ashley Barnes

Bibliographic record

VenueJournal of International Dispute Settlement · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsCompensation (psychology)Context (archaeology)Meaning (existential)International lawLawPolitical scienceInternational arbitrationCollateralWork (physics)Law and economicsArbitrationSociologyHistoryEpistemologyEngineeringPhilosophyPsychology

Abstract

fetched live from OpenAlex

Abstract There is growing interest in the push for individuals to seek direct remedies, notably compensation, for violations of international law. Yet, there is more for scholars to glean from the historical antecedents of compensation for individuals as a recurring idea in international law. The notion of ‘international claims’ can be traced back to the late 18th century. International claims commissions began as ad hoc arrangements that tended to follow wars. In contrast to existing historical accounts, however, the author shows there is more at work in the international law precedents for present-day mass claims than a simple rise and fall of a single idea of compensation, with the occasional isolated contemporary resurfacing. The author contends instead that the history of compensation is deeply linked to its context—meaning different things in different periods—and reflected in four models of compensation (Collateral Private Claims, Arbitration–Diplomatic Protection, Reparations and Institutional-Transition).

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)0.004
metaresearch head score (Gemma)0.006
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0060.017
Scholarly communication0.0130.009
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.240
Teacher spread0.199 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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