Competitiveness Of Production In The Mexican Fishing Sector: The Tuna Case, Competitividad De La Produccion En El Sector Pesquero Mexicano: El Caso Del Atun
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".