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Record W3173457058 · doi:10.5539/jpl.v14n4p19

Land in Liberia: The Initial Source of Antagonism Between Freed American Blacks and Indigenous Tribal People Remains the Cause of Intense Disputes

2021· article· en· W3173457058 on OpenAlexvenueno aff
Stephen H. Gobewole

Bibliographic record

VenueJournal of Politics and Law · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsCustodiansIndigenousAppropriationAccountabilityPolitical scienceLand grabbingLanguage changeGovernment (linguistics)Land tenurePublic landEconomic growthPolitical economyPublic administrationDevelopment economicsGeographyLawSociologyEconomics

Abstract

fetched live from OpenAlex

This study examines factors of land grabbing in Liberia, especially from tribal communities, due originally to different social expectations regarding land and contracts between indigenous people and settlers from America. In addition, land appropriation throughout the history of the Liberian nation is due largely to the Americo-Liberian oligarchy and public corruption. The study analyzes survey, empirical, and concession contracts data gathered by the Ministry of Internal Affairs, Sustainable Development Institute, Government of Liberia, Center for Transparency and Accountability in Liberia, and United Nations Mission in Liberia. It then correlates associations between a number of concession companies, their land acreage under operation, county acreage, and incidence of land grabbing to demonstrate an increase in disputes during the early 2000s due to practices of corrupt public officials. This has resulted from the consistent implementation of inequitable land laws, which have perpetuated land transfer from tribal communities to mostly Americo-Liberian descendants and foreign concessionaires. This land appropriation has fostered public corruption, increased land related disputes, and raised the level of conflict in Liberian society.

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 score0.033
Threshold uncertainty score0.939

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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