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Record W4385612169 · doi:10.47065/jbe.v4i2.3636

Pengaruh Jerat Stagflasi Pada Capaian Laju Investasi di Provinsi Jawa Timur Periode 2019-2022

2023· article· en· W4385612169 on OpenAlexaboutno aff
Anggun Wida Prawira, Syamsul Arifin

Bibliographic record

VenueJournal of Business and Economics Research (JBE) · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsStagflationInflation (cosmology)Investment (military)Realization (probability)EconomicsQuarter (Canadian coin)StatisticsMathematicsGeographyMacroeconomicsUnemploymentPhysicsPolitical science

Abstract

fetched live from OpenAlex

This study aims to determine the effect of the threat of stagflation on investment realization in East Java Province in 2019-2022. The data studied is quarterly secondary data (time series) from the first quarter of 2019 to the fourth quarter of 2022 with a total sample of 16 quarters (for each variable), sourced from the Central Statistics Agency (BPS) and the Investment and One-Stop Services Office (DPMPTSP). Data analysis techniques used descriptive statistics, classical assumption tests, statistical tests and multiple linear regression analysis which were processed using SPSS version 25 software. The results of this study obtained the Stagflation Threat of GRDP Economic Growth with a Sig. 0.990 > 0.05 so it partially has a positive but not significant effect on investment realization. As a result of obstacles to economic performance such as the Covid-19 pandemic. The Threat of Stagflation Inflation obtained Sig. 0.013 <0.05, so partially has a positive and significant effect on investment realization. East Java was able to rise amid rising inflation. The Threat of Stagflation of Economic Growth (GRDP) and Inflation obtained a Sig value of 0.027 <0.05 so that they simultaneously have a positive and significant effect on investment realization.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.256
Teacher spread0.204 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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