Implications of peri-urban land reform programs on urban land markets: a case study of Harare, Zimbabwe
Bibliographic record
Abstract
Zimbabwe implemented the Fast-Track Land Reform Program (FTLRP) in the year 2000. This program has had far-reaching implications on urban development, especially on urban land markets in cities and towns. This paper highlights the implications of the land reform program on urban land markets, using Harare as a case study. The paper is based on document review (policy, academic and development literature) and interviews with key informants with experience and knowledge of the land reform and its associated implications on urban land markets. Our analysis demonstrates that the land reform process in peri-urban spaces was complex and largely driven by political interests. The land reform process distorted the urban planning processes, leading to disparities in the land markets. Such disparities include informal and uncontrolled processes of accessing land and, ultimately, rapid development of informal settlements in the city. Political elites and land barons took advantage of the land reform program extorting money from desperate home-seekers. We conclude that the distortion of urban land markets by politically driven land reform processes negatively affects sustainable urban development. Overall, the paper makes a valuable contribution to the growing literature on land reforms and peri-urban development in Africa, demonstrating the complex and multidimensional implications of land reform programs on peri-urban land markets. Based on the study findings, the paper provides a set of policy recommendations aimed at improving the effectiveness and equity of land reform programs in African peri-urban areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".