Land grabbing in pastoral areas: insights from Eastern Africa
Bibliographic record
Abstract
This paper explores the drivers of land grabbing in pastoral areas. We present a series of cases from across Eastern Africa to illustrate the dynamics through which long-ignored drylands are reimagined by governments and investors as sites of great value, setting the stage for alienation of rangelands at the expense of the pastoral populations who depend on them. Contextualized against the backdrop of colonial and post-colonial development policies, and the ideologies that underpin them, we discuss four resource complexes driving large-scale acquisitions of pastoral lands in East Africa in recent decades: 1) land grabbed via land markets through privatization and subdivision, 2) land acquired for resource extraction, carbon offsetting, and renewable energy production, 3) large-scale alienation of land for commercial agriculture, and 4) land set aside for wildlife conservation (i.e., “green grabbing”). We explore overlapping themes between these four processes that have resulted in the appropriation of pastoral lands, undermined local tenure security, and fragmented landscapes. We highlight in particular bureaucratic dimensions of privatization and land subdivision, reductionist cost-benefit assessments of resource exploitation projects shaped by capitalist logics, the pervasive influence of classical development theory and the associated prioritization of intensified production systems in rural land use policies, and a dualistic Euro-American ideology of nature and society underlying attempts to grab and reclassify pastoral areas for other purposes. Based on these insights, we offer recommendations for ways to mitigate the risks of future land grabs including strengthening pastoral land rights, creating more equitable community-led conservation initiatives, prioritizing participation in development negotiations, and establishing regional policies that support pastoralist livelihoods and maintain rangeland connectivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".