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Record W4242343867 · doi:10.21098/bemp.v15i4.429

QUARTERLY ANALYSIS: The Progress of Monetary, Banking and Payment System, First Quarter – 2013

2013· article· en· W4242343867 on OpenAlexaboutno aff
Author Team of Quarterly Report Bank Indonesia

Bibliographic record

VenueBulletin of Monetary Economics and Banking · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsQuarter (Canadian coin)Investment (military)Consumption (sociology)Capital goodMonetary economicsInternational economicsMarket economyGoods and services

Abstract

fetched live from OpenAlex

Indonesia’s economy in the first quarter 2013 growth slowed compared to the previous quarter. Economic growth stood at 6.02% (yoy), lower than the previous quarter grew by 6.11% (yoy). A source of slowing growth came from domestic demand amid declining export performance. Slowing growth in household consumption was due to the decrease in purchasing power as a result of an increase in inflationary pressures, especially food. In addition, government consumption growth is relatively low, due to the limited uptake of spending, especially spending on goods. A decline also occurred in investment performance, particularly non-construction that is influenced by limited domestic and international demand outlook. Decline in investment performance is in line with the decline in business optimism. In non-construction investment, there is reduced performance in machinery investment, in line with the slowdown in the imports of capital goods. In contrast, exports showed improvement, supported by strengthening expectations of global economic recovery and rising volume of world trade. Response to slowing domestic demandsaw a contraction in imports. Sources of downward import pressure are from the imports of raw materials and capital goods, mainly raw materials for the industrial and passenger vehicle industry which has seen a slowdown and moderation in response to motor vehicle sales.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.164
Teacher spread0.155 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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