Bibliographic record
Abstract
Abstract International human rights language has swept across the landscape of contemporary world politics in a trend that began in the 1970s, picked up speed after the Cold War's end, and quickened yet again in the latter half of the 1990s. Yet, while this human rights `talk' has fundamentally reshaped the way in which global policy elites, transnational activists, and some national leaders talk about politics and justice, actual impacts are more difficult to discern, requiring more nuance and disaggregation. Importantly, there may be substantial cross-regional variations, due to varying colonial and post-colonial histories, and different trajectories in state—society relations. In some instances, there are also important differences in tone between qualitative and quantitative researchers. While many case-study scholars tend to be rather optimistic about the potential for human rights change, statistically inclined researchers often lean towards greater caution and, in some cases, downright skepticism about the trans-formative potential of international human rights law and advocacy. Given that international human rights treaties, human rights reporting, democracy, and elections do not always influence state practice in expected ways, the authors call for more regionally disaggregated studies, coupled with greater efforts to combine qualitative and quantitative research techniques.
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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 | 0.041 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".