Measuring Progress: Evaluating the Use and Added Value of Indicators for Children’s Rights Compliance
Bibliographic record
Abstract
Abstract During the last decades, the use of indicators as tools for assessing states’ compliance with children’s human rights legal provisions included in the related international treaties has rapidly multiplied, both at national and international levels, intended mainly to support policy and strategy reforms to increase social justice, well-being and the implementation of some specific rights. These indicators in the human rights sector build on the use of indicators in the global governance sector. As Sally Engle Merry explains in her ethnography of indicators, the use of indicators in global governance today is primarily derived from economics and business management, even though their roots as modes of knowledge and governance date back several centuries to the establishment of modern nation-states in the early nineteenth century and some centuries before that. For example, the gross domestic product is one of the most extensively used and recognised indicators. Development organisations like the World Bank have developed a wide range of indicators, including measures of global governance and the rule of law (Merry, 2011).
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".