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Record W4308887533 · doi:10.1080/1540496x.2022.2119804

Oceans Apart? China and Other Systemically Important Economies

2022· article· en· W4308887533 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmerging Markets Finance and Trade · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International Affairs
Fundersnot available
KeywordsChinaEconomicsBusinessEconomyEconomic geographyGeography

Abstract

fetched live from OpenAlex

China has been considered a systemically important economy for at least a decade. As policymakers worldwide grapple with sluggish growth there is relatively little evidence about whether the G4, which consists of the US, the Eurozone, Japan, and includes China, as a block contributes to global economic performance in a manner that is not possible when China is left out or treated exogenously. We estimate a series of panel factor and standard VARs because these are well suited to exploit cross-country links. We estimate the relative impact of domestic and global factors on these four economies. First, it is essential to treat China in a model of the G4, on a level playing field with the US, the Eurozone, and Japan to better understand how shocks among these economies interact with each other. Second, we find that domestic and global shocks can reinforce each other. Indeed, global monetary shocks explain up to 60% of variation in commodity demand and real economic conditions. We also report that there is a trade-off between domestic monetary and financial conditions. We recommend that policymakers to reexamine the potential benefits from greater policy cooperation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.189
Teacher spread0.170 · 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