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Record W2079048029 · doi:10.1080/00074910012331338893

Indonesia's Non-Oil Export Performance During the Economic Crisis: Distinguishing Price Trends from Quantity Trends

2000· article· en· W2079048029 on OpenAlexaboutno aff
L. Peter Rosner

Bibliographic record

VenueBulletin of Indonesian Economic Studies · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDepreciation (economics)EconomicsLiberian dollarCurrencyExchange rateUs dollarValue (mathematics)Quarter (Canadian coin)Monetary economicsOil priceCrude oilInternational economicsAgricultural economicsMarket economyGeography

Abstract

fetched live from OpenAlex

Despite an enormous currency depreciation, the growth rate of Indonesia's non-oil exports, measured in dollars, did not accelerate during the first two years of the Asian crisis. In fact, during the second year of the crisis non-oil export value dropped sharply. This paper demonstrates that the main reason for the decline in the dollar value of non-oil exports was a collapse of export prices. Non-oil export dollar prices fell 26% between the second quarter of 1997 and the second quarter of 1999. Measured at constant prices, non-oil exports grew 24% and manufactured exports 31% during this period. Non-oil import prices fell by roughly the same amount as non-oil export prices during the crisis, with little change in the non-oil terms of trade. The decline in the price of traded goods significantly reduced the magnitude of the real exchange rate depreciation experienced by Indonesia.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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

Citations20
Published2000
Admission routes1
Has abstractyes

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