The Global Land Rush and Agricultural Investment in Ghana: Existing Knowledge, Gaps, and Future Directions
Bibliographic record
Abstract
The large-scale acquisition of land by investors intensified following the 2007/2008 triple crises of food, energy, and finance. In the years that followed, tens of millions of hectares of land were leased or sold for agricultural investment. This phenomenon has resulted in a growing body of scholarship that seeks to explain trends, institutional regimes, impacts, and the variety of actors involved, among other subtopics, such as impacts on food security and livelihoods. Focusing on the case study of Ghana, this paper presents a review that uses both quantitative and qualitative methods to critically assess the state of large-scale land acquisitions for agricultural development in Ghana. Our objective in this review is to provide an overview of what we know about such acquisitions in Ghana while pointing to gaps and directions for future research. Contrary to the perception of large-scale land acquisitions being undertaken by foreign investors, the review shows there is a significant role of Ghanaian investors. Additionally, we found the negative impact of these acquisitions, specifically biofuel projects, which featured predominantly in the literature captured in this study. In addition, the role of traditional authorities (chiefs) was a central focus of studies dedicated to land acquisitions in Ghana. Areas that are either understudied or missing from the literature include conflicts, climate change, biodiversity, corporate social responsibility, gendered social differentiation, ethnicity, and the role of diaspora. These gaps call for future research that examines the land question from a multidimensional and multidisciplinary perspective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".