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Record W4312606527 · doi:10.24036/ecosains.11572657.00

Interaksi Faktor Eksternal dengan Siklus Finansial di Indonesia

2020· article· en· W4312606527 on OpenAlexaboutno aff
Yollit Permata Sari, Isra Yenni, Doni Satria

Bibliographic record

VenueEcosains Jurnal Ilmiah Ekonomi dan Pembangunan · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness cycleIndex (typography)IndonesianQuarter (Canadian coin)CommodityEconomicsCapital (architecture)Financial systemConsumption (sociology)Capital marketFinancial marketMonetary economicsFinanceBusinessMacroeconomics

Abstract

fetched live from OpenAlex

This study aims to interact global financial market fluctuations, commodity price fluctuations and capital flows to determine which factors interact with the cycle in Indonesia the most. The data used is secondary data from official publications, namely Bank Indonesia for data on capital flows and bank credit, the International Monetary Fund (IMF) for international commodity price index data and the global financial market valatility index (VIX). The analysis period in this study is the first quarter of 1993 to the fourth quarter of 2018. The results of the study using the correlation index show that the cycle of capital flows in Indonesia is carried out by global finance and The movement of commodity prices causes the flow of funds that enter the Indonesian economy to be largely short-term, so it tends to have acyclical interactions with bank credit to the business world and is slightly more procyclical for credit to non-business fields. Credit to non-business fields is dominated by bank credit, which is allocated to consumption credit and has a short term.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.034
GPT teacher head0.210
Teacher spread0.176 · 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.

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
Published2020
Admission routes1
Has abstractyes

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