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Record W2110323790 · doi:10.4000/vertigo.736

Le foncier en  pratique

2007· article· fr· W2110323790 on OpenAlexvenueno aff
Cédric Vermeulen, Alexandre Lamon, Barnabé Kabore, Alain Lankoande

Bibliographic record

VenueVertigO · 2007
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Le Burkina Faso est le seul pays d’Afrique occidentale dont la législation autorise la gestion cynégétique de la faune par les populations locales. L’opportunité de construire une conservation de la faune servant directement le développement local existe donc. Dans la pratique des choses, il reste cependant encore beaucoup d’étapes à franchir avant que des Zones Villageoises d’Intérêt Cynégétiques (ZOVIC) réellement autonomes et indépendantes financièrement ne prennent corps. La gestion cynégétique villageoise recouvre un ensemble d’enjeux variés et ouvre la porte à de nombreux questionnements : internes à la communauté d’abord, dans son rapport à l’espace et au foncier ; quant au fonctionnement des structures locales de gestion et leur fusion avec le système politique coutumier local ensuite, ainsi que de la réelle volonté de l’univers administratif et privé de la chasse à partager la rente avec les populations locales. Burkina Faso is the only country in western Africa where the official Legislation gives right to the local population to manage wildlife. Thus an Opportunity exists to build a wildlife conservation that can help the local development. In practice, many steps must be still gotten over before ZOVIC (Villages areas for managing wildlife) can become financially independent. Lots of stakes are taken into account in wildlife management and lead to many questions : first inside the community , in his relationship with space and land tenure, then about the managing of local structures, their merger with the local, political and customary system. Finally the real volunty of the administrative and private system of the hunting to deal with the local populations for dividing the income.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0130.009
Open science0.0010.007
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0470.013

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 head0.013
GPT teacher head0.222
Teacher spread0.210 · 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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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Same venueVertigOSame topicAfrican Botany and Ecology StudiesFrench-language works237,207