How do practitioners characterize land tenure security?
Bibliographic record
Abstract
Abstract Improving land tenure security (LTS) is a significant challenge for sustainable development. The Sustainable Development Goals and other recent global initiatives have renewed and increased the need to improve LTS to address climate change, biodiversity loss, food security, poverty reduction, and other challenges. At the same time, policymakers are increasingly interested in evidence‐based policies and decisions, creating urgency for practitioners and researchers to work together. Yet, incongruent characterizations of LTS (identifying the key components of LTS) by practitioners and researchers can limit collaboration and information flows necessary for research and effective policymaking. While there are systematic reviews of how LTS is characterized in the academic literature, no prior study has assessed how practitioners characterize LTS. We address this gap using data from 54 interviews of land tenure practitioners working in 10 countries of global importance for biodiversity and climate change mitigation. Practitioners characterize LTS as complex and multifaceted, and a majority of practitioners refer to de jure terms (e.g., titling) when characterizing it. Notably, in our data just one practitioner characterized LTS in terms of perceptions of the landholder, contrasting the recent emphasis in the academic literature on landholder perceptions in LTS characterizations. Researchers should be aware of incongruence in how LTS is characterized in the academic literature when engaging practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".