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Record W4242531975 · doi:10.1108/oxan-db213123

Czech monetary policy will shift in 2017

2016· other· en· W4242531975 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2016
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsCzechStimulus (psychology)EconomicsPurchasing powerMonetary policyPrivate consumptionQuarter (Canadian coin)Fiscal policyCurrent accountMonetary economicsInterest rateQuantitative easingEconomic policyInternational economicsExchange rateMacroeconomicsCentral bank

Abstract

fetched live from OpenAlex

Subject The outlook for Czech monetary policy. Significance With second-quarter GDP growth slowing to 2.5% year-on-year, the Czech Republic is no longer the fastest-growing Central-East European (CEE) economy. The cyclical upswing that characterised the Czech economy from early 2014 has come to an end. Growth is foreseen to decelerate further in the quarters ahead, owing to unfavourable base effects and a larger-than-expected drop in EU-funded investment and manufacturing inventories. The government is thus expected to introduce short-term fiscal stimulus measures to support consumption. Impacts CEE central banks are expected to hold their rates at current historically low levels for the next quarter at least. Without room for further cuts, few will consider rate tightening before Q1 2017, when the ECB will start winding down quantitative easing. Growth deceleration in the second half of 2016 will necessitate fiscal stimulus by the government ahead of the 2017 general election. Further rises in nominal wages and a firmer labour market will bolster purchasing power, with private consumption the main driver of growth.

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)0.002
metaresearch head score (Gemma)0.006
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: Commentary · Consensus signal: none
Teacher disagreement score0.094
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0940.103

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 head0.042
GPT teacher head0.257
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 designNot applicable
Domainnot available
GenreCommentary

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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