Africa's Land Rush
Bibliographic record
Abstract
Africa has been at the centre of a "land grab" in recent years, with investors lured by projections of rising food prices, growing demand for "green" energy, and cheap land and water rights. But suchland is often also used or claimed through custom by communities. What does this mean for Africa? In what ways are rural people's lives and livelihoods being transformed as a result? And who will control its land and agricultural futures? The case studies explore the processes through which land deals are being made; the implications for agrarian structure, rural livelihoods and food security; and the historical context of changing land uses, revealing that these land grabs may resonate with, even resurrect, forms of large-scale production associated with the colonial and early independence eras. The book depicts the striking diversity of deals and dealers: white Zimbabwean farmers in northern Nigeria, Dutch and American joint ventures in Ghana, an Indian agricultural company in Ethiopia's hinterland, European investors in Kenya's drylands and a Canadian biofuel company on its coast, South African sugar agribusiness in Tanzania's southern growth corridor, in Malawi's "Greenbelt" and in southern Mozambique, and white South African farmers venturing onto former state farms in the Congo. Ruth Hall is Associate Professor at the Institute for Poverty, Land and Agrarian Studies (PLAAS) at the University of the Western Cape, South Africa; Ian Scoones is a Professorial Fellow at the Institute of Development Studies (IDS) at the University of Sussex and Director of the ESRC STEPS Centre; Dzodzi Tsikata is Associate Professor at the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana, Legon.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".