Human Rights INGOs and the North–South Gap: The Challenge of Normative and Empirical Learning
Bibliographic record
Abstract
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. Despite these successes, however, the work of human rights INGOs (most of which are based in the West) is increasingly underscored by operational challenges and questions over their legitimacy in the global South where they do much of their work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".