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Record W4405138315 · doi:10.9734/jeai/2024/v46i123115

Factors Influencing Export of Soya Bean Oil from India: A Panel Gravity Model Analysis

2024· article· en· W4405138315 on OpenAlexaboutno aff
G. Yasaswini, S. M. Trivedi, Jagruti D. Bhatt, N. M. Thaker

Bibliographic record

VenueJournal of Experimental Agriculture International · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityGravity model of tradeOpenness to experienceEconomicsExchange ratePanel dataSoya beanPer capitaOrder (exchange)Agricultural economicsEconomyGeographyInternational tradeInternational economicsEconomic growthMonetary economicsBiologyFood science

Abstract

fetched live from OpenAlex

Soya bean, the ‘miracle bean’, belongs to the family Leguminoceae, subfamily Papilionoidea. Soya bean oil is one of the most widely consumed cooking oils and is rich in linolenic acid. In the past 20 years the export of soya bean oil from India was mainly focused on Bhutan, Jordan, Canada, Singapore and Myanmar. The data was collected from 2002-03 to2021-22. The sources of data were Food and Agriculture Organization Statistics (FAOSTAT), Directorate General of Commercial Intelligence and Statistics (DGCIS), International Monetary Fund (IMF), Statista and Trade map. The panel data was estimated by the Feasible Generalized Least Squares (FGLS) method. For soya bean oil the per capita GDP of India's trading partners, distance, trade openness, and exchange rates are the most significant factors affecting bilateral trade. While partner countries' prosperity and liberal trade policies boost trade, greater distances and unfavorable exchange rates hinder it. India's GDP and domestic inflation show minimal impact on trade flows. Wooldridge test indicated no first-order auto-correlation.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.037
GPT teacher head0.267
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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