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The The Impact of the Renegotiation of United States–Mexico–Canada Agreement (USMCA) on the Agricultural Exports of Sinaloa State of Mexico

2020· article· en· W3048082668 on OpenAlexaboutno aff
José́ G. Vargas-Hernández, Icela Flores Osuna, Omar C. Vargas-González

Bibliographic record

VenueLogistics & Supply Chain Review · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFree trade agreementAgricultureInternational tradeTreatyInternational economicsState (computer science)Investment (military)Per capitaBusinessTrade agreementForeign direct investmentEconomicsGovernment (linguistics)Agricultural economicsFree tradePolitical scienceGeographyMacroeconomics

Abstract

fetched live from OpenAlex

Purpose: Mexico, like other countries, invested in measures to attract foreign direct investment to its territories. It, therefore, signed the North American Free Trade Agreement (NAFTA) in 1994, a treaty that facilitated Mexico to be the largest direct exporter to the United States. However, in 2018 the agreement was renegotiated and replaced with United States–Mexico–Canada Agreement (USMCA). This research is carried out to determine the advantages and disadvantages of renegotiation for Sinaloa's agricultural exports, with the question of whether it would negatively impact the Sinaloa's agricultural exports.
 Methods: The study focuses on the impact of renegotiation of the NAFTA on agricultural exports of the state of Sinaloa with indicators such as the Exports-Trade, GDP, and GDP Per capita of Mexico, opening to new markets, and logistics.
 Results: The renegotiation has a direct relationship with agricultural production in Sinaloa, with a serious negative effect, since overproduction would be created if the new destination for exporting from Sinaloa was not quickly available.
 Implications: This research can be of much use to the main agricultural exporting companies in Sinaloa, government agencies, and the Sinaloa Chambers of Commerce for decision making and policy formulation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.036
GPT teacher head0.238
Teacher spread0.202 · 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
Published2020
Admission routes1
Has abstractyes

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