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Record W2741763305 · doi:10.21272/fmir.1(1).72-79.2017

The role of tax competition between the countries of the world and the features of determining the main tax competitors of Ukraine among the European countries

2017· article· en· W2741763305 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

VenueFinancial Markets Institutions and Risks · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsCompetitor analysisTax competitionCompetition (biology)BusinessCluster (spacecraft)EconomicsIndirect taxTax reformPublic economicsMarketing

Abstract

fetched live from OpenAlex

The article defines the role of the tax system, which it plays to gain competitive advantages of the country in social and economic spheres. The system of relevant taxes, characterizing the level of competition of the national tax system, is identified. Methodological basis for determining tax countries competing with Ukraine based on the cluster analysis is improved. By implementing the algorithm for establishment the European countriestax competitors of Ukraine, firstly, potential tax competitors of Ukraine were identified, secondly, real tax competitors of Ukraine in a cluster with a similar structure of the tax system in terms of socioeconomic development were set.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.010
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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