Effects of Large-Scale Acquisition on Food Insecurity in Sierra Leone
Bibliographic record
Abstract
The recent phenomenon of large-scale acquisition of land for a variety of investment purposes has raised deep concerns over the food security, livelihood and socio-economic development of communities in many regions of the developing world. This study set out to investigate the food security outcomes of land acquisitions in northern Sierra Leone. Using a mixture of quantitative and qualitative research methods, the study measures the severity of food insecurity and hunger, compares the situation of food security before and after the onset of operations of a land investing company, analyzes the food security implications of producing own food versus depending on wage labour for household food needs, and evaluates initiatives put in place by the land investing company to mitigate its food insecurity footprint. Results show an increase in the severity of food insecurity and hunger. Household income from agricultural production has fallen. Employment by the land investing company is limited in terms of the number of people it employs relative to the population of communities in which it operates. Also, wages from employment by the company cannot meet the staple food needs of its employees. The programme that has been put in place by the company to mitigate its food insecurity footprint is failing because of a host of reasons that relate to organization and power relations. In conclusion, rural people are better off producing their own food than depending on the corporate structure of land investment companies. Governments should provide an enabling framework to accommodate this food security need, both in land investment operations that are ongoing and in those that are yet to operate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".