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African Court on Human and Peoples’ Rights

2018· book-chapter· en· W2909253782 on OpenAlexaff
Nicole de Silva

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsLawPolitical scienceInternational courtHuman rightsInternational lawInternational human rights lawMandatePrecedentCommon lawPublic international law

Abstract

fetched live from OpenAlex

This chapter examines international courts’ premises as objects of international law through the case of the African Court on Human and Peoples’ Rights. When creating an international court, states become legally obligated to supply its physical premises—a functional and symbolic resource that underpins the court’s legal authority and influence. Drawing on archival evidence, this chapter analyses the African Court’s significant challenges in securing this important resource from political actors within the African Union and Tanzania, the court’s host state. It shows that, with international law and courts, there can be a considerable gap between states’ commitment and compliance, and between legal ambition and political reality. This gap, however, can mobilize court officials to assert their needs for resources and, more generally, the significance of their mandate. Examining international courts’ premises, therefore, can elucidate the tensions between law and politics embedded in international justice specifically and international law more broadly.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0180.002

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 head0.040
GPT teacher head0.254
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2018
Admission routes1
Has abstractyes

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