Medir progresos en educación en derechos humanos: Una experiencia interamericana en marcha
Bibliographic record
Abstract
Since the year 2000, the Inter-American Institute of Human Rights (IIHR) has been developing a new research methodology on human rights based on a system of progress indicators about three groups of rights: access to justice, political participation and human rights education. The approach was initially applied in 6 countries of the region, and produced the Progress Maps on Human Rights. This experience set the foundations for the annual preparation of the Inter-American Report on Human Rights Education, which IIHR distributes every December 10th, since 2002. The paper explains the oldest and more widely used approaches for research on human rights: (i) the registration of violations and (ii) the analysis of human rights situations. Then, it introduces the approach of measuring progress, its tools (progress indicators), the main methodological considerations, and the application of this approach, up to date, in 19 countries of the American continent that subscribed and/or ratified the San Salvador Protocol. Such application constitutes the first two Inter-American Reports on Human Rights Education, which are part of a series of 4 reports. El main objective of the series is to investigate the variations produced regarding the incorporation of Human Rights Education in formal and non-formal education, in the selected countries, during the period 1990-2002/03. The I Report (2002) focused on the legal developments of Human Rights Education at the national level, and the II Report (2003) examined the advanced in the curriculum and the textbooks in the elementary and high school levels of the formal education system. The conclusions and recommendations of both Reports are transcribed in the appendices.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".