Competitiveness of Vietnam’s Frozen or Processed Tuna Export Industry in the United States and Canada Markets
Bibliographic record
Abstract
Purpose: This study aims to assess the competitiveness of Vietnam’s frozen and processed tuna export industry (code 160414) in the US and Canada markets. Design/methodology/approach: In this study we used the Revealed Comparative Advantage index (RCA), export market share (MS) and export prices on a comparative basis with 9 of the 10 largest tuna exporters, and based on the theory of absolute advantage and comparative advantage to assess the competitiveness of the fish export industry tuna in the US and Canada markets. Findings: Research results show that the competitiveness of Vietnamese tuna in the US and Canada markets is quite high, second only to Thailand and is tending to increase. Originality/value: The research’s findings highlight the importance of enhancing product quality, catering to consumer demands in importing nations so that Vietnam’s tuna export products are in line with import market preferences, and taking advantage of opportunities provided by the CPTPP agreement to benefit from preferential tariffs when exporting to Canada in order for Vietnam to increase competitiveness and increase export scale in these two markets. To boost the competitiveness of Vietnam’s tuna export business, particularly for the US market, it is critical to have trade promotion solutions, extend trade cooperation to benefit from advantageous tariffs, and match the product needs of importing nations. The long-term objective for Vietnam’s tuna export sector must be for sustainable and effective development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".