A Methodological Framework for Comparative Land Governance Research in Latin America and the Caribbean
Bibliographic record
Abstract
Strengthening land governance is critically needed in Latin America and the Caribbean to protect the environment, achieve gender equality in land rights, expand the transparency of land records, and facilitate planned urban growth. Inadequate land administration limits the development of housing markets, tax collection, and the scale and speed of housing and land regularization programs in low-income communities. The region faces major challenges in land tenure informality and overlapping mandates for titling, mapping, and registration. In response to these issues, this technical note identifies the gaps in land governance information for five Latin American and Caribbean countries (Barbados, Brazil, Ecuador, Panama, and Trinidad and Tobago), and provides a comparative methodological framework for field research in these countries. The annex provides Spanish and Portuguese translations of the questionnaire, which includes new questions absent from existing tools, such as the World Bank's Land Governance Assessment Framework and USAID's Blueprint for Strengthening Real Property Rights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".