This Is My Grand Pa’s Land: Land, and Development Projects and Evictions along Morogoro Highway, Tanzania
Bibliographic record
Abstract
This article addresses one of the most contentious development and political issue facing Tanzania and many Southern African countries. The focus is on privatization of land as a result of neo-liberal economic reforms, evictions and conflicts generated. The study was conducted along Morogoro highway, in Dar es Salaam and Pwani regions. The study employed qualitative approaches where archival information, interview observations and focus group discussions were used to collect data. Key findings indicate that there is a gap between laws and policies of land, which are designed to protect customary land rights of indigenous communities and individuals, and the actual practices regarding land on the ground. Despite the multi-party system democratic reforms, ordinary people have not turned their elected representatives at the local, regional levels or NGOs as allies in the efforts to resist land evictions. To understand development as a concept and its outcome when translated into action, to see what is happening on the ground, I draw part of my arguments from Social Movements theories to understand local people’s reactions toward development programs that result into their evictions. The study also explored the relationship among key land stakeholders in Tanzania and analyzed how uncoordinated relationships and the state officials lead to conflict. The study revealed that there is gender inequality in land access and ownership and how women used their position as women to frame resistance and attract not only media but also government and international community. A central argument in this study is that for land development program to benefit the targeted population, all land actors from grassroots to top should be involved in the process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".