Transforming Land-Related Conflict: Policy, Practice and Possibilities
Bibliographic record
Abstract
From Introduction: "What are some of those challenges? What approaches to land-related conflicts exist at the local level, and what is being learned from them in practice? What are international donor agencies doing at the interface of land and conflict? How are global civil society networks grappling with these issues? What could these and other actors do to promote the transformation of land-related conflicts? This paper reflects on these questions through a review of primary documents and secondary literature. Section 2 analyzes current debates and practices on the resolution of land-related conflicts. Section 3 examines emerging efforts in selected donor agencies, and scans what four global civil society networks are doing at the nexus of land and conflict. Section 4 brings these threads together to identify some areas for reflection and action by different actors. Other dimensions, such as national strategies, regional initiatives, the role of the private sector and other land policy challenges, are only addressed in passing due to time and space limits. Through this joint product, the International Land Coalition and The North-South Institute aim to inform the efforts of a range of different actors trying to enhance their responses to challenges at the crossroads of land and conflict. Our emphasis is on practical options for these actors, but issues not amenable to easy solutions are also raised to provoke deeper reflection."
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".