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Record W4415949647 · doi:10.1080/13600818.2025.2584014

Women and land ownership in peri-urban Dar es Salaam – land policy reforms and cultural facades

2025· article· en· W4415949647 on OpenAlexaff
Said Nuhu, N. Macha, Wilbard Kombe, Chakupewa Joseph Mpambije, Francis Dakyaga, Rose Shayo

Bibliographic record

VenueOxford Development Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Studies and Geopolitics
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsDar es salaamLand tenureLand registrationTanzaniaLand lawLand rights

Abstract

fetched live from OpenAlex

Women’s land ownership remains a contemporary discourse in scholarship, especially from Sub-Saharan African countries. Using the case of peri-urban areas of Dar es Salaam, this study explores the disconnect between land policy reforms and culture, underscoring their contrasting forces in redefining the reality concerning women’s land ownership. A mixed-methods case study design employing structured questionnaires, in-depth interviews, focus group discussions and document analysis enabled data collection. There are still significant inequalities in land ownership attributable to social and cultural norms in peri-urban areas, even after several legislative and policy reforms in Tanzania were made to facilitate land ownership for men and women. Systemic reforms and restructuring to foster effective land policy reforms for equity in the ownership of land, especially in societies with deep-rooted norms and values, are recommended. Land-focused advocates and lobbyists are needed to influence policy reforms that are gender-neutral and which can steer societal transformation away from patriarchal mindsets.

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)0.001
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.334
Teacher spread0.299 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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