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Record W4252312794 · doi:10.1108/oxan-db245821

Japan's fiscal stimulus will mute tax hike impact

2019· other· en· W4252312794 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2019
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsStimulus (psychology)GDP deflatorReal gross domestic productUnemploymentQuarter (Canadian coin)Monetary economicsChinaReal wagesLabour economicsInternational economicsMacroeconomicsGeography

Abstract

fetched live from OpenAlex

Subject The macroeconomic outlook for Japan. Significance Real GDP rose 1.8% in the April-June quarter of 2019 (seasonally adjusted, annualised), supported by household consumption and company investment, which jumped 6.1%. The one negative area was trade, where import growth had a negative effect on GDP. Nevertheless, the 6.7% jump in imports after last quarter’s collapse speaks to underlying demand strength. Part of the rise in household consumption can be attributed to the scheduled October 1 increase of the national consumption tax to 10%, from 8% currently. Impacts Washington’s trade war with Beijing threatens Japanese high-tech exports of parts used in China-assembled devices. Low unemployment rates mask considerable spare labour supply among part-time workers. Employers are not yet compelled to offer higher real wages or better working conditions. Despite offsetting fiscal stimulus, the tax rise will still shift demand from some sectors to others. Monetary policymakers will be heartened by fiscal stimulus, but still confront weak inflationary pressures indicated by a flat GDP deflator.

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)0.005
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 score0.095
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0950.039

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 head0.017
GPT teacher head0.249
Teacher spread0.233 · 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
Published2019
Admission routes1
Has abstractyes

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