NGOs, international courts, and state backlash against human rights accountability: Evidence from NGO mobilization against Tanzania at the African Court on Human and Peoples' Rights
Bibliographic record
Abstract
Abstract When nongovernmental organizations (NGOs) encounter state resistance to human rights accountability, how do NGOs use international courts for their human rights advocacy strategies? Considering the overlapping phenomena of shrinking civic space within authoritarian, hybrid, and democratically backsliding regimes, and state backlash against international courts, NGOs navigate two potential levels of state backlash against human rights accountability. Building on the interdisciplinary scholarship on legal mobilization, we develop an integrated framework for explaining how states' two-level (domestic and international) backlash tactics can both promote and deter NGOs' strategic litigation at international human rights courts (IHRCs). States' backlash tactics can influence NGOs' opportunities, capacities, and goals for their human rights advocacy, and thus affect whether and how they pursue strategic litigation at IHRCs. We elucidate the value of this framework through case studies of NGOs' litigation against Tanzania at the African Court on Human and Peoples' Rights, an understudied IHRC. Drawing on an original data set, interviews, and documentation, we process-trace how Tanzania's various backlash tactics influenced whether and how NGOs litigated at the Court. Our framework and analysis show how state backlash against human rights accountability affects NGOs' mobilization at IHRCs and, relatedly, IHRCs' opportunities for influence.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".