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Record W2885992806 · doi:10.1017/s0022278x18000289

Land investments in Tanzania: assessing the role of state brokers

2018· article· en· W2885992806 on OpenAlexaff
Joanny Bélair

Bibliographic record

VenueThe Journal of Modern African Studies · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTanzaniaPossession (linguistics)IntermediaryLocal governmentState (computer science)Context (archaeology)BusinessLand tenureCentral governmentEconomicsEconomic systemFinanceGeographyPolitical sciencePublic administrationSocioeconomics

Abstract

fetched live from OpenAlex

Abstract Focusing on one of the most targeted areas for land investments in Tanzania (Rufiji district), this article compares the involvement of two Tanzanian state agencies in land acquisition, in the context of the central government's new strategy on productive investors. Given the fragmented and contentious authority of many African states, I investigate the impact of state intermediaries on the relationships between investors and local populations and consider bureaucrats as a group of actors to analyse flows of power within the state. I make two main points. First, the central state's weak infrastructural power and resulting lack of local knowledge, and, conversely, local bureaucrats’ possession of these valuable resources, reverses the flow of power from local to central. Second, a central monitoring process might have a negative effect. Instead of protecting vulnerable populations, it fosters institutional innovations that protect local bureaucrats’ opportunities for accumulation with investors, to the detriment of local populations.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.029
GPT teacher head0.261
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

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