Gulf Cooperation Council Countries and the Global Land Grab
Bibliographic record
Abstract
A rapid increase in large-scale land acquisitions associated with the food-commodity price spike in 2008 resulted in a flurry of journalistic, non-governmental organization, and academic publications. One of the primary narratives that emerged was that oil-rich Gulf states were driving a “land grab” from resource-poor countries. However, little was known about who was making deals and where. This article assesses the extent to which the member states of the Gulf Cooperation Council (GCC) are, in fact, primary players. We first compare the total number of deals and land areas involved, finding that individual GCC member states have been relatively minor players compared to the United States, the United Kingdom, China, Singapore, and Malaysia—each of whom, moreover, finalized more deals than all the GCC countries put together. We next compare the geographic distribution of acquisitions, comparing the trends for GCC member states with those of the major investing countries, and assess which countries have acquired land from the most financially constrained nations. We conclude with a critical discussion that reflects on the narrative of oil-rich Gulf states as a driving force behind the global land grab and the potential reasons for its prominence.
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 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 | 0.001 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".