Strategic spatial planning in emerging land-use frontiers: evidence from Mozambique
Bibliographic record
Abstract
Land-use frontiers are territories with abundant land for agriculture and forestry, availability of natural resources relative to labor or capital and predisposed to rapid land-use change, often driven by large-scale land investments and capitalized actors, producing commodities for distal markets. Strategic spatial planning (SSP) represents a consolidated long-term governance practice across high- and low-income countries. One of the objectives of SSP processes is to articulate a more coherent and future-oriented spatial logic for the sustainability of land-use patterns and typologies, natural-resources protection, and investments. SSP may thus constitute a useful approach in addressing some of the challenges affecting land governance in frontier settings; to date, its potential contribution to land-use frontiers lacks explicit exploration. In this paper, we examine how SSP can play a role in governing land-use frontiers through a case-study analysis of Mozambique as an emerging frontier, located on the southeast coast of Africa. We gathered empirical evidence by interviewing experts involved in resource management, territorial planning, and development in the country. The theoretical spine of the paper builds on the literature focusing on land-use challenges and SSP. We show that emerging land-use frontiers face several challenges, such as transnational land deals and the intensification of commercial plantations. Interview data show that several structural factors are hindering the establishment of a long-term territorial development strategy. These are, among others, the short-termism of political cycles and the absence of a long-term strategic vision. Our analysis reveals that SSP processes could contribute to addressing land-use challenges in frontier contexts, such as poverty traps and land degradation spirals, should various local and distant actors join forces and marry interests. We conclude by presenting a systematic rationale, explaining how SSP could play a role in governing land-use frontiers, with a view to promoting the well-being and sustainability of rural communities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".