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Record W7083199600 · doi:10.4000/14qqo

Introduction - Environmental conflicts and community management of natural protected areas in French-speaking Africa

2024· article· en· W7083199600 on OpenAlexaffvenue

Bibliographic record

VenueVertigO · 2024
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsUniversité de MonctonUniversité du Québec à Montréal
Fundersnot available
KeywordsPoliticsNatural resourceRiparian zoneNatural resource managementProtected areaAppropriationBiodiversity conservationEcosystem management

Abstract

fetched live from OpenAlex

In most French-speaking countries of sub-Saharan Africa, environmental conservation areas are largely the legacy of colonial occupation. For more recent protected areas, their conceptualization, establishment and management programs are almost exclusively carried out by international players, in more or less close collaboration with national authorities, and with varying degrees of consultation and integration of local communities living in or near these environmental spaces. Presented as international development projects, these protected areas are for the most part created ex nihilo, and are only weakly integrated into the territories and their socio-environmental, political and economic structures. This introduction to the thematic issue provides a non-exhaustive synthesis of the literature on protected areas, environmental conflicts and the prospects for community integration and management by and for riparian communities. It examines how, on the one hand, the endogenous environmental, social and political norms and practices of riparian communities can contribute to the conservation of biodiversity and, on the other, how their integration into the management processes of natural protected areas can help strengthen environmental conservation programs and the appropriation of the environmental services they can provide.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.291

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 head0.012
GPT teacher head0.219
Teacher spread0.206 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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