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Record W4312171743 · doi:10.37801/ajad2022.19.2.5

Fish and Fishery Products Trade by India: Trends, Competitiveness, and Comparative Advantage

2022· article· en· W4312171743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Journal of Agriculture and Development · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsRevealed comparative advantageFisheryFish productsComparative advantageBusinessFish <Actinopterygii>Dried fishCommodityIndex (typography)International tradeGeographyBiology

Abstract

fetched live from OpenAlex

This study analyzes the trends and determines the comparative advantage and competitiveness of India’s fish and fishery products trade in the world market and of India’s exports to 10 major destinations over the period of 2000–2021. We use the revealed symmetric comparative advantage (RSCA) index to quantify India’s comparative advantage in exporting fish and fishery products and the Vollrath index to measure the revealed competitiveness of the country’s fish and fishery products trade. We collected relevant data at Harmonized System (HS) four-digit level from the UN Commodity Trade (UN Comtrade) database. Our analysis shows that India has a revealed comparative advantage (RCA) in exporting fish and fishery products to the world market. Specifically, India has a comparative advantage in exporting frozen fish, crustaceans, and mollusks; but it has a comparative disadvantage in exporting live fish, fresh and chilled fish, fish fillets and other fish meat, and dried/salted/in-brine and smoked fish to the world market. In terms of individual destinations, India has RCA in exporting live fish to Hong Kong; fresh and chilled fish to UAE (in recent years); frozen fish to China, Hong Kong, Thailand (recent years), and Vietnam (recent years); fish fillets and other fish meat to Japan (recent years); dried fish to Hong Kong; crustaceans to Japan, the US, and Canada (recent years); mollusks to the EU, Thailand (recent years), and Vietnam (recent years); and other aquatic invertebrates to Vietnam. India has a comparative disadvantage (RCD) in exporting fresh and chilled fish to the EU, Japan, the US, and Vietnam, and fish fillet and other fish meat to the US, Canada, and Vietnam. The COVID-19 pandemic has negatively affected India’s export, comparative advantage, and trade competitiveness of fish and fishery products. India’s RCA and competitiveness in exporting fish and fishery products decreased in 2018–2020, but the RCA and competitiveness increased by 2021.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.193
Teacher spread0.181 · 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