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Record W1998810460 · doi:10.1177/0027950109103674

The World Economy: Economic downturn in Asia

2009· article· en· W1998810460 on OpenAlexaboutno aff
Dawn Holland, Ray Barrell, Tatiana Fic, Ian Hurst, Iana Liadze, Ali Orazgani, Vladimir Pillonca

Bibliographic record

VenueNational Institute Economic Review · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)EconomicsSlowdownChinaRecessionInvestment (military)Consumption (sociology)Real gross domestic productSlow growthAgricultural economicsCapital (architecture)International economicsMonetary economicsMarket economyMacroeconomicsGeographyEconomic growth

Abstract

fetched live from OpenAlex

Recent economic developments in Asia have proved significantly worse than anticipated. Year-on-year GDP growth in China slowed to 6.8 per cent in the final quarter of 2008, the slowest rate of growth since 1991 and well below the government's target rate of growth of 8 per cent that is required to maintain a stable employment rate. It is difficult to establish the source of the Chinese slowdown with the available data. Fixed capital investment continued to expand rapidly, and the trade surplus in value terms rose to record high levels in the final quarter of the year. This suggests that consumption was weak, although retail sales figures indicate strong growth in the final quarter of the year. We have assumed that the slowdown observed in the fourth quarter was driven primarily by domestic demand. The collapse in global trade this year will have a significant impact on Chinese exports, given that nearly 50 per cent of Chinese goods exports are in the machinery and transport equipment sector. We expect GDP growth to average about 5½ per cent this year, with growth reverting above 8 per cent per annum only in 2012.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.292
Teacher spread0.254 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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