MétaCan
Menu
Back to cohort
Record W2790572900

Europa und die Weltwirtschaft: Globale Konjunktur ist weiter gedämpft

2016· article· de· W2790572900 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
FieldEconomics, Econometrics and Finance
TopicGerman Economic Analysis & Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPaceChinaQuarter (Canadian coin)Emerging marketsConsumption (sociology)RecessionGlobal recessionCommodityWorld economyDevelopment economicsInternational economicsMarket economyGeographyPolitical scienceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Die Weltwirtschaft kommt weiterhin nicht in Fahrt. Nach dem bereits schwachen Jahresende 2015 hat sich das Expansionstempo im Auftaktquartal 2016 erneut verlangsamt. In den Schwellenländern dürfte die Konjunktur auch weiterhin gedämpft bleiben. Vor allem in China setzt sich die graduelle Wachstumsabschwächung im Zuge des Abbaus von Überkapazitäten fort. Russland und Brasilien dürften in der Rezession bleiben; neben den nach wie vor niedrigen Rohstoffpreisen tun hausgemachte Probleme ihr Übriges. Die entwickelten Volkswirtschaften können dies nicht ausgleichen, da die Konjunktur hier lediglich stabil verläuft. Hauptstütze bleibt dort die Binnennachfrage. Vor allem in den USA, aber auch im Euroraum ist mit kräftigen Konsumzuwächsen zu rechnen, die insbesondere aus der sich verbessernden Lage am Arbeitsmarkt resultieren. Unter dem Strich dürfte am Ende dieses Jahres mit 3,2 Prozent ein geringeres Wachstum der globalen Wirtschaftsleistung stehen als noch zuletzt erwartet. Insbesondere die Unsicherheit über die weitere konjunkturelle Entwicklung in China und die möglichen Auswirkungen eines Brexits bremsen den Optimismus.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0040.006
Scholarly communication0.0120.008
Open science0.0010.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0410.007

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 head0.220
Teacher spread0.204 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEconstor (Econstor)Same topicGerman Economic Analysis & PoliciesFrench-language works237,207