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Record W1517130069 · doi:10.1155/2014/563021

A Step Prior to REDD+ Implementation: A Socioeconomic Study

2014· article· en· W1517130069 on OpenAlexafffund
Anne Bernard, Nancy Gélinas

Bibliographic record

VenueInternational Journal of Forestry Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsDeforestation (computer science)GeographySubsistence agricultureThreatened speciesPopulationEnvironmental resource managementBusinessLand tenureNatural resource economicsEnvironmental planningEnvironmental protectionAgricultureEconomicsHabitatEcology

Abstract

fetched live from OpenAlex

Phase 2 of the United Nations’ REDD+ climate change mitigation initiative is underway in the Democratic Republic of Congo. Meanwhile, activities are being implemented to assess the reduction of emissions from deforestation and forest degradation. REDD+ projects need to include a social dimension; thus, the aim of this research was to understand how land-use relationships vary across communities in an area where a REDD+ project is planned. Specifically, we aimed to identify the primary income-generating activities, the variation in access to land, the potential for the development of community projects, and the implementation of alternative income-generating activities. In the summer of 2013, we assessed a REDD+ pilot project in and around the Luki Biosphere Reserve, Bas-Congo Province. We used participatory rural appraisal (PRA) methods in four communities located both inside and outside the reserve. We found that current subsistence income activities led to the destruction of forest habitat due to population pressure and a lack of alternative income-generating activities. Customary land tenures overlay statutory rights, which can often mean that community rights are threatened. To achieve their targets, REDD+ projects should consider the actual land-use patterns of local communities in order to generate sustainable income from the land.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.385
Teacher spread0.340 · 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 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
Published2014
Admission routes2
Has abstractyes

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