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Record W1521279337 · doi:10.22004/ag.econ.50892

EFFECTIVENESS OF BYLAWS IN THE MANAGEMENT OF NATURAL RESOURCES: The West African Experience

2008· preprint· en· W1521279337 on OpenAlexfundno aff
Koffi Alinon, Antoine Kalinganiré

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2008
Typepreprint
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
FundersInternational Fund for Agricultural DevelopmentConsortium of International Agricultural Research CentersWorld Bank GroupWorld Agroforestry CentreInternational Development Research CentreInternational Fine Particle Research Institute
KeywordsDecentralizationNegotiationDelegationPublic administrationEnforcementNatural resourceContext (archaeology)Political scienceLanguage changeNatural resource managementLaw enforcementBusinessEconomic growthLawGeographyEconomics

Abstract

fetched live from OpenAlex

The role of various stakeholders in the management of natural resources is not clear in the West African countries. This paper discusses the historical changes in power delegation from central origins to peripheral institutions. The analysis covers the rise of bylaws across the Western African countries and links the multiplicity of bylaws to the amplification of the decentralization movement. On the basis of a literature review and their own practitioners’ experiences, the authors demonstrate the pertinence of bylaws as a tool for better management of natural resources. In the West African Francophone context, bylaws could stand both for regulations enacted by decentralized authorities or “local conventions” binding village community groups. Where formal bylaws suffer from limited enforcement, local people continue, through their traditional representatives, to engage in the negotiation of local conventions for the management of natural resources. According to the authors, there is a need to recognize local conventions, which offer an opportunity for decentralization to be more rooted in local situations. Through such conventions, traditional institutions prove their ability to reshape with decentralization even if decentralization reforms and national forestry laws have ignored them across West Africa.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.032
GPT teacher head0.273
Teacher spread0.241 · 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.

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

Citations4
Published2008
Admission routes1
Has abstractyes

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