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Record W2485368049

Global growth still subdued

2016· article· de· W2485368049 on OpenAlexaboutno aff
Ferdinand Fichtner, Guido Baldi, Christian Dreger, Hella Engerer, Christoph Große Steffen, Michael Hachula, Malte Rieth, Thore Schlaak

Bibliographic record

VenueEconstor (Econstor) · 2016
Typearticle
Languagede
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsChinaPaceRecessionEmerging marketsConsumption (sociology)Quarter (Canadian coin)Developed countryInternational economicsCommodityWorld economyGlobal recessionMomentum (technical analysis)BrexitDevelopment economicsMacroeconomicsMarket economyEuropean unionFinance
DOInot available

Abstract

fetched live from OpenAlex

The world economy has yet to regain momentum: after the already weak final quarter of 2015, the pace of expansion slowed down again in the first quarter of 2016. In the emerging countries' economies, growth is expected to remain subdued, especially in China, where the gradual slowdown continues as overcapacities are reduced. Russia and Brazil are likely to remain in recession: apart from the still-low commodity prices, domestic issues are aggravating the situation. Growth is just barely stable in the industrialized countries, which means they cannot compensate for the emerging countries' weaknesses. In the industrialized countries, the primary growth driver is still domestic demand. Strong consumption growth is expected in the US as well as in the euro area, primarily as a result of the improving labor market situation. All in all, the global economic growth rate is expected to be 3.2 percent in 2016, which is lower than previously forecasted. The uncertainty about China's future economic development and the potential impact of a Brexit are the primary risks that are curbing optimism.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0160.017

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

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