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Record W2893485004 · doi:10.1002/mde.2968

“Regulate me not”: The regulatory failures of taxation: A tale from Greece

2018· article· en· W2893485004 on OpenAlexaff
Constantina Kottaridi, Dimitrios D. Thomakos

Bibliographic record

VenueManagerial and Decision Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsAllocative efficiencyRestructuringEconomicsLanguage changeTax reformState (computer science)Public economicsEconomic systemMarket economyMicroeconomicsFinanceComputer science

Abstract

fetched live from OpenAlex

This paper is a critical review of the problems of the Greek tax system. A well‐structured tax system promotes allocative efficiency and supports economic growth, whereas on the contrary current Greek reality translates to nontransparency, complexity, and tax corruption: all of them constitute regulatory failure. Using data, which refer to detailed tax information on certain years, starting in 2006 and ending in 2011, and data on the total back taxes owed to the Greek state, we highlight both the problems but more importantly the need to restructure the Greek tax system to finally promote economic growth and not crony capitalism.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.013
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.215
Teacher spread0.189 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2018
Admission routes1
Has abstractyes

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