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Record W3011887584 · doi:10.3390/math8030451

Corporate Performance and Economic Convergence between Europe and the US: A Cluster Analysis Along Industry Lines

2020· article· en· W3011887584 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMathematics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsBishop's University
FundersBishop's University
KeywordsHomogeneousConvergence (economics)European unionValuation (finance)Economic geographyCluster (spacecraft)BusinessEconomic systemEconomicsEconomyInternational tradeInternational economicsAccountingMacroeconomics

Abstract

fetched live from OpenAlex

We investigate the extent to which the United States and the countries of Europe have achieved economic convergence of their corporate sector. We define convergence as the homogenization of economic performance, institutional arrangements, and market valuation taking place at the meso-economic level. We perform a cluster analysis along industry lines and find that industries and corporations on both continents cluster in four groups, based on six variables measuring operating performance, ownership, and market valuation. The clusters resulted from the US data are more unstable than those resulted from European data. We are also able to pair a handful of highly similar clusters between the US and European data. These findings suggest a complex dynamic. It seems that the US corporate sector is more homogeneous than the European one. Moreover, some degree of convergence between the European Union and the United States appears to have already occurred.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.211
Teacher spread0.153 · 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