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Record W2016186239 · doi:10.1080/00045600903202855

Territorial Tensions: Rainforest Conservation, Postconflict Recovery, and Land Tenure in Liberia

2009· article· en· W2016186239 on OpenAlexaff
Leif Brottem, Jon D. Unruh

Bibliographic record

VenueAnnals of the Association of American Geographers · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsRainforestPolitical scienceGovernment (linguistics)Land useNature ConservationCustomary landEnvironmental planningLand tenureCivil societyGeographyNatural resource economicsEnvironmental protectionEnvironmental resource managementDevelopment economicsEconomicsEcologyPoliticsLawAgriculture

Abstract

fetched live from OpenAlex

Since the cessation of civil conflict in 2003, the Liberian government is poised to expand greatly its protected area network to conserve the country's remaining rainforest. Liberia holds within its borders nearly half of the remaining Guinean rainforest, a global biodiversity hotspot. The planning process for this effort has been a central part of rebuilding Liberia's forestry sector, which helped fuel past conflict. The process, known as the Liberia Forestry Initiative, is widely considered to have been exemplary with regard to multistakeholder dialogue and policy making. Because the issue of land tenure remains widely contested, however, the initiative risks seriously aggravating land rights problems, complicating the prospects for a durable peace. This article focuses on the process through which land was zoned for strict protection and how this process is likely to exacerbate land tenure conflict in Liberia's priority conservation areas. Although the international conservation community sees postconflict scenarios as opportunities for promoting conservation initiatives, unresolved land tenure issues make for problematic outcomes, including land disputes and legal disarray. Such problems can result in significant volatility and can make the peace process and recovery much more, not less, difficult.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations36
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Association of American GeographersSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207