The System of Land Ownership and Its Effect on Agricultural Production: The Case of Ghana
Bibliographic record
Abstract
Most African continents have pressing issues on individual rights to property and natural resources, given the relatively poor economic conditions and the belief of personal ownership to a property right (Joireman, 2008). Ghana, like many African countries like Mozambique and Uganda, have laws to the right of property that is the traditional system of land rights. Most of the African countries depend on the large share of natural capital from the natural resources for the economic growth of the country. Some emerging economies can have sustained economic growth due to their reliance on natural resources such as oil and gas. This paper investigates property rights, land ownership, and land inheritance and their effect on agricultural production in Ghana. To undertake this research, a sample of 35 respondents were analysed using the SPSS software. The analysis was based on characteristics such as gender, age, and educational level of the respondents. The research results indicate that men inherit more than women, and family ownership is the most popular type of land inheritance in Ghana. Also, people with a lower level of education are likely to inherit the land and own land. Finally, the patrilineal system is the most popular system of inheritance in Ghana.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".