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Record W2101119900 · doi:10.1177/0020715207072158

The Effects of Primary Sector Foreign Investment on Carbon Dioxide Emissions from Agriculture Production in Less-Developed Countries, 1980-99

2007· article· en· W2101119900 on OpenAlexvenueno aff
Andrew K. Jorgenson

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentProduction (economics)AgricultureInvestment (military)Primary sector of the economyCarbon dioxideNatural resource economicsEconomicsBusinessInternational tradeInternational economicsEconomic sectorEconomyMacroeconomicsEcologyPolitical science

Abstract

fetched live from OpenAlex

This research helps to increase our collective understanding of the complex interrelationships between foreign investment dependence and environmental degradation. Panel regresssion analyses of 35 less developed countries from 1980 to 1999 are conducted to test the hypothesis that foreign direct investment in the primary sector increases carbon dioxide emissions from agriculture production. Results confi rm the hypothesis, providing support for the theory of foreign capital dependence. Level of agriculture production and the use of tractors are also found to increase the growth of carbon dioxide emissions from this primary sector activity. Conversely, nations more likely to ratify international environmental treaties exhibit suppressed growth in emissions. These fi ndings underscore the need for social scientists to investigate the environmental impacts of both the level and transnational organization of production in different sectors as well as the overall use of relevant machinery and the environmental commitments of nation-states.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations65
Published2007
Admission routes1
Has abstractyes

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