The Rush for Land and Agricultural Investment in Ethiopia: What We Know and What We Are Missing
Bibliographic record
Abstract
More than a decade has passed since the triple crises of food, energy and finance in the period 2007–2008. Those events turned global investor interest to agriculture and its commodities and thereafter the leasing of tens of millions of hectares of land. This article reviews and synthesizes the available evidence regarding the agricultural investments that have taken place in Ethiopia since that time. We use a systematic review approach to identify literature from the Web of Science and complement that with additional literature found via Google Scholar. Qualitative and quantitative methods are used to analyze the available literature. In so doing, we raise questions of data quality, by analyzing the evidence base used by many studies (the Land Matrix database) and compare it with data we obtained from the Government of Ethiopia. We find that while the Land Matrix is the largest available database, it appears to present only a fraction of the reality. In critically assessing the literature, we identify areas that have been under-researched or are missing from the literature, namely assessments of gendered impacts, the role of diaspora and domestic investors, interdisciplinary approaches (e.g., integrating climate change, biodiversity, and water), and studies that move beyond technical assessment, such as looking at the impacts on traditional knowledge and socio-cultural systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".