Geospatial technologies in support of responsible land tenure governance: A systematic review
Bibliographic record
Abstract
The importance of land for development and the challenges faced by smallholder farmers and communities have led to a focus on responsible land tenure governance. This has resulted in the evolution of numerous policies and conceptual frameworks globally. In contemporary research, efforts are ongoing to elucidate how geospatial technologies can be utilized to enhance understanding and mapping of outcomes of people-to-land relationships. However, the role and impacts of geospatial technologies in the context of responsible land tenure governance are scantly evidenced. Thus, this paper explores the role of geospatial technologies in supporting responsible land tenure governance. A systematic review of existing literature was employed to independently verify the quality and consistency of the results presented in contemporary literature. The first step of the review process involved searching and identifying relevant and appropriate documents from various databases. Relevant materials from existing literature were selected using inclusion and exclusion criteria. Following these steps, 103 candidate publications were found, of which only 42 (40.80 %) met all the inclusion criteria. The review result shows the variation and increment of publications over time: the majority (19.05 %) were published in 2018, whereas,11.9 % were produced in 2023. The number of publications addressing the potential of geospatial technologies for responsible land tenure governance is increasing rapidly from 2001 to 2018. Overall, the study shows that these technologies positively contribute to responsible land governance, particularly in developing countries. However, to be most likely to achieve success in responsible land tenure governance, future research is needed to devise a holistic mechanism to integrate the knowledge of indigenous institutions with geospatial technologies to promote more equitable and sustainable land tenure governance and address the challenges associated with the application of these technologies in the land sector. • Land tenure focuses on the relationship between people and the land they use. • Geospatial technologies are important for promoting responsible land tenure governance. • Responsible land governance is sustainable & fair land use decisions. • VGGT is Leading framework for responsible land tenure.
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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".