Transnational capital and the scramble for land and profit: financialization, agrarian development, and resource conflict in Africa
Bibliographic record
Abstract
The promise of financialization in the agrarian world in Africa stems from its capacity to (re)create local economies purportedly for the benefit of investors, local communities, and governments. However, financialization is also known to unevenly distribute risks and rewards between investors and local communities. Therefore, this paper employs a just transition lens to systematically examine existing studies on financial investments in economic activities that involve the exploitation of agricultural resources. It does so to generate big picture insights on the relationship between financialized agro-economic activities (FAEAs), agrarian development, and resource conflict in Africa. Informed by an exploratory research design, the paper draws on secondary data on seven case studies (Uganda, Tanzania, Zambia, South Africa, Côte d’Ivoire, Senegal, and Ghana) to outline the different sets of endogenous and exogenous circumstances, practices, and tendencies in the pre-entry, entry, operations, and exit phases of FAEAs that explain how and why they spur development or engender conflict across their lifespans. By examining FAEAs across these phases in multiple countries, we show that the explanations for their developmental and conflictual outcomes transcend causal binaries of settled versus contested resource rights in the pre-entry phase, stakeholders engagement versus lack of engagement in the entry phase, economic integration versus displacement in the operations phase, and inclusive versus exclusive project termination. It shows that the effect of FAEAs on development and conflict are better understood by also considering the different grey areas between each of the aforementioned pair of factors that shape how FAEAs unfold in local communities. By applying a whole-systems perspective on just transition to examining FAEAs, this paper helps to highlight these grey dynamics (e.g., the side effects of integration on food security, long-term financial well-being, and community cohesion), thereby alerting policymakers, development practitioners, and the general public on how and why FAEAs make local communities susceptible to resource conflict in spite of their acclaimed developmental potential.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".