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Record W4401177830 · doi:10.1163/17087384-bja10095

An Assessment of the Impact of Corporate-Driven Foreign Land Investments on Selected Human Rights in Mozambique

2024· article· en· W4401177830 on OpenAlexvenueno aff
Bhavna Mahadew

Bibliographic record

VenueAfrican Journal of Legal Studies · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsSustenanceHuman rightsLivelihoodPoliticsLand grabbingInternational lawRight to foodPolitical scienceEconomic growthBusinessFood securityDevelopment economicsLawEconomicsGeographyAgriculture

Abstract

fetched live from OpenAlex

Abstract This article focuses on the impact of land investments in Mozambique. It examines the effects these land investments are having on local people’ access to food and sustenance, culture, land and water, and political engagement in some Mozambican provinces and areas. The next section critically evaluates whether human rights related to the aspects are being violated, as well as Mozambique’s legal obligations under both local and international law. The main goals are to alert the academic community to the negative impacts that land grab and investments have on local populations in Mozambique and to explain how these activities can be effectively stopped by using the normative framework now in place on human rights. It argues that foreign companies and investors in Mozambique are causing significant negative impacts on local communities, including livelihood, food, culture, land, and political participation. These issues are already protected by human rights law, and effective protection by the state could help combat land grabbing. Mozambique’s judiciary, civil society, and human rights institutions should work together to prevent local communities from being harmed.

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 score0.100
Threshold uncertainty score0.191

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 head0.041
GPT teacher head0.326
Teacher spread0.285 · 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 routes1
Has abstractyes

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