Formalising village land dispossession? An aggregate analysis of the combined effects of the land formalisation and land acquisition agendas in Tanzania
Bibliographic record
Abstract
While literature on land grabbing and land formalisation respectively has literally exploded the past decade, few studies analyse the practical processes taking place at their confluence, or provide an analysis at an aggregate level. This paper is based on 27 months of in-depth empirical investigation of thirteen large-scale agro-investments across four regions in Tanzania. It explores how four key legislative acts and policies related to land formalisation and land acquisition for large-scale agro-investments unfold on the ground, their implementation and combined effects. We show that land formalisation and acquisition are intrinsically linked: the former paving the way for investment in all thirteen cases. Moreover, rather than fulfilling development policy expectations of land tenure security for smallholder farmers, employment and poverty reduction in rural Africa, we demonstrate that, in Tanzania, these combined processes rather foster village land dispossession, investors’ land acquisitions, and a (re)centralisation of land control. Therefore, we argue that the conjoint implementation of policies associated with land formalisation and land investments have adverse consequences for rural farmers whose land is formalised and then set aside for investment ultimately leading to a formalised rural land dispossession. Our unique aggregate analysis thus provides solid support to the existing critique towards the parallel implementation of land formalisation and large-scale agro-investments, and the interlinked reform of the land legislative framework, all strongly supported by global development bodies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".