Green grabs, land grabs and the spatiality of displacement: eviction from <scp>M</scp>ozambique's <scp>L</scp>impopo <scp>N</scp>ational <scp>P</scp>ark
Bibliographic record
Abstract
The Mozambican state is currently working to relocate 7000 people from the interior of the Limpopo National Park (LNP), itself part of the Great Limpopo Transfrontier Park (GLTP). As the process began in 2003, this stands out as one of the region's most protracted contemporary conservation‐related evictions. I draw from this case to shed light on the increasingly complex spatial dynamics of land and green grabs and, more specifically, demonstrate the importance of zooming out from discrete land acquisitions to examine how their resulting displacements are increasingly shaped by spatial processes at and beyond their borders. In doing so, we begin to see that displacement from the LNP is not a simple case of eviction from a discrete protected area. Rather, it has been provoked by the opening of the international border, hence drawing transfrontier conservation areas (TFCAs) like the GLTP into the purview of land and green grabs. At the same time, competition over space with an adjacent grab – a sugarcane/ethanol plantation – has severely interfered with relocation, drastically prolonging it. The case, more broadly, sheds light on how conservation, agricultural extraction and climate change mitigation – all forms of land acquisitions that incite dislocation – come together to produce novel patterns of environmental displacement, placing profound pressures on rural communities and their abilities to occupy space and access resources, including labour opportunities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".