Bibliographic record
Abstract
Over the past two decades, human rights language has spread like wildfire across international policy arenas. The activists who sparked this fire are engaged in two different campaigns. The first is comparatively modest, involving the persuasion of tens of thousands of global elites such as journalists, UN officials, donors, and national political leaders. The second is broader and more complex: to have a real impact on the behavior of tens of millions of state agents worldwide. While most international relations scholars agree that the first campaign has made real gains, opinions are split on the success—past, present, and future—of the second. In part, these divisions fall along methodological lines. With some exceptions, qualitative scholars working in the empirical international relations tradition express more optimism than their quantitative counterparts, whose contributions to the subfield are relatively new. This article reviews several new books on human rights and shows how their insights engage with these ongoing methodological debates. The authors argue that both qualitative and quantitative approaches offer important strengths and that neither has a monopoly on truth. Still, the human rights discourse may be thriving, at least in part, for reasons unrelated to impact. The authors conclude with suggestions for a more systematic and multimethod research, along with a plea for scholarly attention to the potential downsides of international human rights promotion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.204 | 0.067 |
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".