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Record W4252310968 · doi:10.21098/bemp.v19i3.664

QUARTERLY OUTLOOK ON MONETARY, BANKING, AND PAYMENT SYSTEM IN INDONESIA: QUARTER IV, 2016

2017· article· en· W4252310968 on OpenAlexaffabout
TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman, Tri Kurnia Ayu K, Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi

Bibliographic record

VenueBulletin of Monetary Economics and Banking · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMonetary policyInflation (cosmology)Quarter (Canadian coin)EconomicsMonetary economicsExchange rateOrder (exchange)Consumption (sociology)Investment (military)Current accountInflation targetingFinancial systemInternational economicsFinance

Abstract

fetched live from OpenAlex

The Indonesian economy recorded development in Quarter 4, 2016. The growth increased with more sound macroeconomic and financial system stability. The growth was supported by the growth of household consumption, better performance of investment, and the raise of export. On the other hand, the macroeconomic stability is well maintained as reflected on lower inflation, decreasing current account deficit, and stable Rupiah against foreign exchange. Domestic economy improves in accordance with the lower global financial risk and provides room for easing monetary policy on Quarter IV, 2016. The central bank lower the policy rate is well transmitted and is expected to strenghthen the growth momentum of economy ahead. Looking forward however, we still have to keep an eye on several external and domestic risks. For these reasons, Bank Indonesia keeps strengthening its monetary and macroprudential policy mix, and its coordination with the government in order to maintain the macroeconmoic stability, supporting the growth, and accelerate the structural reforms.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.182
Teacher spread0.174 · 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
Published2017
Admission routes2
Has abstractyes

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