Global Distribution of Transnational Human Rights <scp>NGO</scp>s: The Effects of Domestic Resources and Institutions
Bibliographic record
Abstract
This article examines domestic sources of the uneven distribution of human rights transnational NGO s ( TNGO s) across countries. I compile an original dataset covering 787 human rights TNGO s during the 2005–2010 period from the Yearbook of International Organizations (supplemented by the directories produced by Human Rights Internet and the Encyclopedia of Associations ). I employ the zero‐inflated negative binomial model to explore domestic conditions influencing the location of TNGO headquarters. The analysis distinguishes two processes. First, population size and political institutions are particularly important for the likelihood of hosting any human rights TNGO s. Human rights TNGO s are likely to exist only in strong democratic countries with relatively large populations. Second, domestic resources (economic and human) and institutions (political and regulatory) affect the count of human rights TNGO s in a country. A high level of economic development, a large and well‐educated population, strong democratic institutions, and a less regulatory environment provide favorable conditions for the establishment of more human rights TNGO s. Although human rights TNGO s are transnationally oriented, their establishment is still greatly influenced by domestic factors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".