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Record W4394398075 · doi:10.6084/m9.figshare.24814199

The factors affecting Vietnam’s canned tuna exports

2023· dataset· en· W4394398075 on OpenAlexaboutno aff
Nguyễn Hồng Nga, Le Thi Xoan

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsTunaFisheryBusinessFood scienceFish <Actinopterygii>ChemistryBiology

Abstract

fetched live from OpenAlex

In this study, we used the gravity model to identify factors affecting Vietnam’s tuna exports to major import markets, including the United States, Canada, Japan and European countries, and then to find the solutions with sufficient scientific and practical basis to promote the development of the tuna export industry in the future. This research’results show that an increase in factors including domestic tuna production, exchange rates, population of the importing country and geographical distance leads to increase the scale of Vietnam’ tuna exports, with the exchange rate playing the most important role, while the import tax rate is the significant barrier that reduces Vietnam’s tuna exports. In order to develop the tuna export sustainably in the future, Vietnam must maintain tight control over the domestic tuna resources, avoid overexploitation, and instead focus on enhancing product quality. Furthermore, it is critical to focus on satisfying the conditions of commitments in signed free trade agreements, actively analyzing the market and paying attention to trade promotion policies, growing trade connections with importing countries.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.152
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.044
GPT teacher head0.245
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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