MétaCan
Menu
Back to cohort
Record W4235864971 · doi:10.1108/oxan-db253104

Japan’s recession will deepen but recovery will follow

2020· other· en· W4235864971 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2020
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionChinaEconomic recoveryGovernment (linguistics)Quarter (Canadian coin)EconomicsCluster (spacecraft)Coronavirus disease 2019 (COVID-19)Key (lock)BusinessDevelopment economicsEconomyGeographyMacroeconomicsMedicineComputer science

Abstract

fetched live from OpenAlex

Significance GDP is likely to have contracted even more sharply in the second quarter, when the lockdown was at its height. The reopening of China’s economy, about two months ahead of Japan’s, suggests that consumers will be slow to resume old patterns. Impacts International demand and supply will be dampened even if Japan is quick to recover domestically; exports will lag a domestic recovery. Government support of small business and individuals with lost income will be key to recovery. Japan’s ‘cluster-based’ approach that tracks infection sources and isolates high-transmissibility cases appears to have worked.

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)0.003
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0520.016

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 head0.028
GPT teacher head0.229
Teacher spread0.201 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

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