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Record W3084734385 · doi:10.35448/jequ.v10i1.8577

STUDI EMPIRIS PENGARUH HARGA MINYAK MENTAH DUNIA DAN VARIABEL MONETER TERHADAP PEREKONOMIAN INDONESIA PERIODE 1996-2018

2020· article· id· W3084734385 on OpenAlexaboutno aff
Luthfi Fajar Arifah, Muhammad Basorudin, Muhammad Abdul Majid, Mira Choirunnisa, Putri Lydia Eltheofany S

Bibliographic record

VenueJurnal Ekonomi-Qu · 2020
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsQuarter (Canadian coin)EconomicsIndonesianContext (archaeology)Vector autoregressionError correction modelValue (mathematics)Descriptive statisticsEconomyEconometricsGeographyMathematicsStatisticsCointegration

Abstract

fetched live from OpenAlex

Oil is one of the strategic energies in the economy. The fluctuations will always be a favorite barometer of economists and world leaders. Therefore, this study aims to convert deeper into the world and monetary variables towards the Indonesian economy for the period 1996-2018. This study uses secondary data from the IMF and BPS. Variables are economic growth, oil prices, and interest rates. Perform the analysis carried out, namely descriptive analysis using Images and inferential analysis with Vector Error Correction Mechanism (VECM). The results of this study are common in the context of economic growth. Meanwhile, there is no economic growth. Meanwhile, at that time economic growth was influenced by economic growth in the first quarter, second and third quarter, growth in oil prices in the previous quarter, and economic growth in the previous quarter. The average value of ECT produced is negative and significant.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.225
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

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

Citations4
Published2020
Admission routes1
Has abstractyes

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