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Record W2998459279

World Economy Autumn 2018 - Less even growth in the world economy with significant downside risks

2018· article· en· W2998459279 on OpenAlexaboutno aff
Klaus-Jürgen Gern, Philipp Hauber, Stefan Kooths, Saskia Mösle, Ulrich Stolzenburg

Bibliographic record

VenueEconstor (Econstor) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsQuarter (Canadian coin)Inflation (cosmology)World economySlowdownSanctionsConsumption (sociology)Downside riskEmerging marketsMomentum (technical analysis)EconomyMonetary economicsInternational economicsMacroeconomicsFinanceEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

The global economy is losing steam. After a weak start in the beginning of this year, world production accelerated again in the second quarter of 2018. However, the recent expansion probably overstates its underlying momentum. At the same time, the expansion is becoming less even as compared to last year. In addition, global economic prospects remain subject to significant downside risks in light of increasing trade tensions, investors withdrawing funds from emerging markets, and uncertainty concerning the effect of renewed Iran sanctions on oil prices. Our forecast for global growth in 2018 nevertheless remains unchanged at 3.8 percent; for 2019 we slightly revise downwards-by 0.1 percentage points-to 3.5 percent. In 2020 world output is expected to rise by 3.4 percent. Despite the gradual global economic slowdown, capacity utilization in advanced economies will remain high. As a consequence, inflationary pressures will gradually increase beyond the current temporary pick-up of inflation stemming from higher oil prices.

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)0.002
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.276
Teacher spread0.239 · 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
Published2018
Admission routes1
Has abstractyes

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