The export competitiveness of the tuna industry in Thailand
Bibliographic record
Abstract
Purpose Thailand dominates world exports of canned tuna with a market share of around 40 percent which is at least four times higher than any other exporter. The aim of this paper is to examine the export competitiveness of the canned tuna export industry in Thailand for 1996‐2006. Design/methodology/approach The paper uses a revealed comparative advantage (RCA) approach and calculates RCA indices for both major exporters in the world market and competitors in individual export markets. Findings Thailand has comparative advantages in all major export markets; these have remained stable in the USA, the Middle East, Japan and Canada but have fallen substantially in Australia. Practical implications First, Thailand urgently needs to consider tuna farming. Second, smaller processing and fishing companies should merge to increase profit margins and market share. Third, Thailand should engage in effective trade negotiations with importers. Fourth, stock management and conservation could be used to support the industry. It is unlikely that current levels of comparative advantage can be maintained because of import tariffs, rules of origin, labour shortages and increasing unskilled labour costs. Social implications Tuna management and conservation in Thailand could be used to support the sustainability of the industry. Originality/value By contrast to Kijboonchoo and Kalayanakupt who find that Thailand's market share declined between 1987‐1998 and revealed comparative advantage fell, these results show that this declining trend has since been arrested.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".