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Record W4403304345 · doi:10.1177/23210249241281857

Institutional Duality in Land Administration: Insights from Collaborative Governance in Ghana

2024· article· en· W4403304345 on OpenAlexaff
Abdul-Salam Ibrahim, Bernard Afiik Akanpabadai Akanbang, Ibrahim Yakubu, Abraham Marshall Nunbogu, Moses Mosonsieyiri Kansanga, Vincent Kuuire

Bibliographic record

VenueJournal of Land and Rural Studies · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCorporate governanceDuality (order theory)Administration (probate law)Land administrationCollaborative governancePolitical sciencePublic administrationBusinessEconomic systemGeographyEnvironmental planningEconomicsPure mathematicsMathematicsFinance

Abstract

fetched live from OpenAlex

The global drive for collaboration towards addressing society’s growing complex challenges is gaining more credence in land administration. Collaborative land governance is crucial in Africa, where the duality in land governance, as expressed in the coexistence of statutory and customary land governance institutions, has been a longstanding source of land conflicts. Drawing theoretical insights from collaborative governance and using in-depth interviews with stakeholders across both customary and statutory land governance systems, this study examines the interplay of factors that militate against effective collaborative land governance in Ghana. Findings show that while the legislative framework on land administration in Ghana authorises collaboration, the challenges of limited trust and awareness of land laws, poor facilitative leadership and inadequate resources militate against collaborative land governance. We argue that the weak manifestation of the well-intentioned legislative frameworks for collaborative land governance calls for increased attention to implementation gaps in equal footing to policy formulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2024
Admission routes1
Has abstractyes

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