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Perubahan Fluktuatif Struktur Ekonomi Indonesia Pada Masa Pandemi Covid-19

2020· article· en· W3113258629 on OpenAlexaboutno aff
Herlina Herlina

Bibliographic record

VenueAl-Mutharahah Jurnal Penelitian dan Kajian Sosial Keagamaan · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)IndonesianPopulationPandemicCoronavirus disease 2019 (COVID-19)Development economicsDemographic economicsEconomicsGeographyPolitical scienceEconomic growthDemographySociologyMedicine

Abstract

fetched live from OpenAlex

Economic growth in Indonesia has experienced fluctuating changes in 2020, especially entering the Covid-19 pandemic. The subjectivity factor greatly affects Indonesia's economic growth. The important factors that play a role are the low income per person of the population and the large carry-over of the workforce which takes place under pressure from very vulnerable external conditions, which is reflected in the high burden of foreign debt. This is due to being too dependent on imported products which have eroded labor productivity and the empowerment of natural resources has greatly decreased. Economic growth has declined sharply with the spread of the Covid-19 virus which began to enter Indonesia in the second quarter of 2020. The Covid-19 pandemic has greatly affected the movement of the Indonesian economy which tends to decline. This study uses a qualitative method with a descriptive level of explanation, centralized data collection inaccurate social media reports from reliable informant sources, namely the statement of the Minister of Finance Sri Mulyani. The presentation of the research results that in the first quarter (the period from January 2020 to March 2020) economic growth was recorded at a safe rate of plus 2.97%, in the second quarter (April 2020 to June 2020) showed a slump in economic growth, namely minus 5, 32%, there was a significant change, namely a slight increase but still a minus occurred in the third quarter (July 2020 to September 2020) which was minus 1% to minus 2.9%. The negative conditions in the last two quarters resulted in the Indonesian economy falling into a recession. The government must make various important efforts to overcome this recession, namely by deregulating, reducing loan interest rates, increasing interest rates or public savings margins, promoting non-oil and gas exports, expanding job opportunities, restructuring processes due to the still gloomy external situation, increasing taxes, reduction of imported products, handling of the informal sector.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.053
GPT teacher head0.308
Teacher spread0.255 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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