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Record W4293479355 · doi:10.21111/dauliyah.v7i2.8498

THE INCREASING OF WEST SUMATRA EXPORT IN THE GLOBAL MARKET DURING THE COVID-19 PANDEMIC

2022· article· en· W4293479355 on OpenAlexaboutno aff
Silvy Cory, Sofia Trisni, Putriviola Elian Nasir

Bibliographic record

VenueDauliyah Journal of Islamic and International Affairs · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)IndonesianCompetition (biology)BusinessInternational tradeAgency (philosophy)Domestic marketQuarter (Canadian coin)Descriptive statisticsInternational economicsEconomicsGeography

Abstract

fetched live from OpenAlex

The international trade sector, especially exports, has become one of the focuses of the Indonesian government, especially the West Sumatra government. West Sumatra's leading export products such as essential oils, CPO, and rubber products have their own markets in the global market. Data from the West Sumatra Central Statistics Agency shows a significant increase in the export value of West Sumatra from the end of 2019 to the final quarter of 2020. This study aims to look at the factors that support the increase in exports of West Sumatra in the global market during the Covid-19 pandemic. To see these factors this study will use the concept of a resource-based paradigm which will focus on internal factors and the contingency paradigm that focuses on external factors of international trade. This research uses qualitative research methods with a descriptive analysis approach. This study found that internal factors of increasing West Sumatra exports, namely West Sumatra government policies that support exports, the experience of local companies, and the performance of these two parties. Meanwhile, internal factors are the conditions of competition between countries in increasing production and the increasing global demand for food and medicinal products.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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