MétaCan
Menu
Back to cohort
Record W4385337794 · doi:10.1596/978-1-4648-1960-5_ch2

Land-Based Commons: The Basis for a Peaceful Form of Economic Development?

2023· book-chapter· en· W4385337794 on OpenAlexaff
Mathieu Boche, Patrick D’Aquino, Nicolas Hubert, Stéphanie Leyronas, Sidy Mohamed Seck

Bibliographic record

VenueThe World Bank eBooks · 2023
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCommonsPoliticsNatural resourceNormativePolitical scienceCorporate governanceSustainable developmentSovereigntyOrder (exchange)SociologyLaw and economicsLawEconomicsManagement

Abstract

fetched live from OpenAlex

Examines how the various forms of land-based commons in Sub-Saharan Africa can provide the basis for a type of economic development that preserves social stability and enables the sustainable use of natural resources by analyzing this dynamic in three stages: (1) discussing issues related to preserving natural resources in and reviewing the evolution of the normative regimes that define and regulate land-based commons in order to characterize these commons in terms of the resources they offer, the rights and arrangements on which they depend, and the social organizations responsible for their governance; (2) demonstrating how land-based commons face a series of transformations that change the social and political regimes regulating their management and access; and (3) providing a survey of the guarantees of security and support that these modes of managing and using natural resources require to support peaceful and sustainable economic development, in particular ways to rethink the involvement of states and local authorities.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.037
GPT teacher head0.220
Teacher spread0.183 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe World Bank eBooksSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207