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Record W1602041653 · doi:10.1017/cbo9780511511233.005

Human Rights INGOs and the North–South Gap: The Challenge of Normative and Empirical Learning

2006· book-chapter· en· W1602041653 on OpenAlexaff
Bonny Ibhawoh

Bibliographic record

VenueCambridge University Press eBooks · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsBrock University
Fundersnot available
KeywordsHuman rightsPolitical scienceHuman rights movementInternational human rights lawRight to propertyLinguistic rightsGlobalizationFundamental rightsEnforcementLawPublic administration

Abstract

fetched live from OpenAlex

The role of human rights International Nongovernmental Organizations (INGOs) has become increasingly important in an age of globalization in which they are seen as heralding a global civil society and a new world order based on a universal human rights. INGOs have been at the forefront of the “human rights revolution” – a revolution of norms and values that has redefined our understanding of ethics and justice. They have shaped the course of the human rights movement not only at the international level but also at regional and national levels. INGO involvements in global transnational networking, particularly in the 1980s and 1990s, have been crucial to the development of the universal human rights corpus as well as its enforcement and monitoring mechanisms. One reason for the growing influence of INGOs within the human rights movement has been their ability to build transnational coalitions and mobilize global action on key human rights issues. This is evident in the role of INGOs in such human rights milestones as the 1992 Second World Conference on Human Rights in Vienna, the establishment of a United Nations (UN) High Commission for Human Rights, and the establishment of the International Criminal Court. If the UN midwifed the postwar universal human rights movement, INGOs have weaned and nurtured it.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.994
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.243
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 teacher head, not a consensus.

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

Citations4
Published2006
Admission routes1
Has abstractyes

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