Minding ‘Productive Gaps’: An Appraisal of Non-operational Land Deals in Seven Sub-Saharan African Countries
Bibliographic record
Abstract
One of the dominant global development agendas for rural Africa in the past two decades has cast large-scale agro-industrial investments as a solution to achieve more efficient land use, higher crop yields, enhanced food security, and poverty reduction, among others. However, mounting evidence shows that this agenda has not fulfilled its promises: most land deals for agricultural production have not materialised as planned and their socio-economic development objectives often remain unreached. Despite the often severe impacts of non-operational projects, knowledge about why they fail to take place and operate remains fragmentary. Based on an extensive literature review of contemporary land deals in seven sub-Saharan countries, this paper sheds light on two ‘productive gaps’. First, the article delves into the ‘productive gap’ of land deals themselves, identifying key drivers of non-operational land deals. The reviewed literature points to local opposition and financial difficulties as significant factors impacting agricultural operations. Local opposition, in turn, stems largely from flawed land acquisition processes and unfulfilled investors’ promises. Second, this article offers a critical appraisal of the biases and oversights in the knowledge the land grab scholarship has ‘produced’.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".