MétaCan
Menu
Back to cohort
Record W4241139268 · doi:10.1108/oxan-db244084

Japan economic trends point to slowdown or recession

2019· other· en· W4241139268 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2019
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionQuarter (Canadian coin)EconomicsConsumer confidence indexChinaPessimismSlowdownConsumption (sociology)Stimulus (psychology)Economic slowdownEconomic recoveryInternational economicsMacroeconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Subject The macroeconomic outlook for Japan. Significance For the first time in six and a half years, businesses are pessimistic about the economic outlook, according to the results of a Nikkei-Markit survey of manufacturing purchasing managers released today. This comes just days after Japan’s first-quarter 2019 real GDP data surprised forecasters with a solid 2.1% growth over the previous quarter (seasonally adjusted, annual rate). Nominal growth was an even more surprising 3.3%. Impacts Even a recession is unlikely to deter a tax rise; the government would increase the immediate stimulus accompanying the rise. New US tariffs on China will mean lower exports from China to the United States, in turn reducing Chinese demand for Japanese components. Growth of jobs and labour income could boost consumer sentiment eventually, reversing an 18-month slide in confidence and consumption.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0900.053

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 head0.033
GPT teacher head0.269
Teacher spread0.236 · 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
GenreOther

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

Explore more

Same venueEmerald expert briefingsSame topicRegional resilience and developmentFrench-language works237,207