Agri-Business Development in Cameroon: Colonial Legacies and Recent Tensions
Bibliographic record
Abstract
Although Cameroon was not a prime target in the modern-day scramble for Africa, the Central African country has been a site of intense land-related tensions in the past decade. According to data from the Land Matrix, 2,771,406 hectares were subjected to land deals since the year 2000. By late 2019, 44 deals were registered as ‘concluded,’ covering 2,065,998 hectares of the area under negotiation. The main drivers are timber, biofuel crops, food products, and precious minerals. This chapter focuses on agro-industrial projects and provides a review of recent trends in land acquisitions that is grounded in critical development studies. Based on a mixed-methods approach combining an analysis of Land Matrix data and the study of legal frameworks with field work data, it aligns trends in land acquisitions with domestic politics and regulatory changes in the Republic of Cameroon. The chapter advances three key insights. First, the land rush in Cameroon was not a sudden phenomenon, and it emerged prior to the commodity price hike of the late 2000s. Second, conflictual land relations are the result of ineffective regulatory frameworks. Third, domestic actors, rather than foreign corporations, are of central importance for determining the outcomes of land-related long-term investments in sub-Saharan Africa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".