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Record W2770810826

Competitiveness Of Production In The Mexican Fishing Sector: The Tuna Case, Competitividad De La Produccion En El Sector Pesquero Mexicano: El Caso Del Atun

2017· article· es· W2770810826 on OpenAlexaboutno aff
Jorge Quiroz Félix, Manuel de Jesús Barra Valenzuela, Julio Alfonso Merino Payan, Martha Ofelia Lobo Rodríguez

Bibliographic record

VenueRevista Global de Negocios · 2017
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisExportationTunaFishingBusinessCompetition (biology)Emerging marketsProduction (economics)Index (typography)International tradeEconomicsEconomyFisheryFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

This document presents how some countries are losing participation over time in the international market of selected fishery products. Specifically we examine the tuna market which is moving to new emerging economies in the competition for international markets. This paper illustrates the Japanese market as the destination and the participation of their principal suppliers of tuna. We contrast the competitive performance between Mexico and its competitors: United States, Canada and Spain. We utilize the method of constant market shares in the period of 2001-2012. In the first section we address the historical importance of fish exportation, especially tuna for Mexico in the Japanese market for the last twelve years. We show participation of the fishing sector in the exports of the Mexican food industry sector, and how is has been an important sector within agro-alimentary exports. A second section shows how the fishing sector, despite being important in Mexican exports, has a positive competitiveness index. It reveals some negative fluctuations but with signs of recovery in recent years. In recent years this sector gained competitiveness in the international market relative to emerging exporting countries. These emerging countries new competitors for the Japan market

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.286
Teacher spread0.269 · 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.

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
Published2017
Admission routes1
Has abstractyes

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