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Record W3041517711 · doi:10.18192/potentia.v10i0.4510

Foreign Direct Investment and Extractive Institutions

2019· article· en· W3041517711 on OpenAlexaffvenue
Jack Bowness

Bibliographic record

VenuePotentia Journal of International Affairs · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsCarleton University
Fundersnot available
KeywordsForeign direct investmentNatural resourceContext (archaeology)Latin AmericansEconomicsArgument (complex analysis)Quality (philosophy)BusinessInternational economicsDevelopment economicsPolitical scienceMacroeconomicsGeography

Abstract

fetched live from OpenAlex

There is a significant debate underway regarding the risks and rewards of foreign direct investment (FDI) for countries in the Global South. These discussions are particularly relevant to the people of Latin America, where the use of inward FDI as a mechanism to support economic development has had dramatic results, both positive and negative. One of the key works in the study of FDI is Robert I. Rotberg’s argument that FDI is critical to support the development of weak states; however, the applicability of this theory faces difficulty in the context of Latin America, where middle-income countries have extractive institutions (Rotberg, 2002). I use the cases of Mexico and Peru to demonstrate that for middle-income countries, extractive institutions can hamper the rewards of FDI and even exacerbate development problems or create new ones. In this regard, the sector of FDI will determine the nature of the impact. In states with extractive institutions, FDI in the natural resource sector is prone to stimulating social conflict. In states with extractive institutions, FDI in the manufacturing sector begets a situation of stagnated development, as the jobs that are introduced are of poor quality and low wages.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.783

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes2
Has abstractyes

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