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Record W2118525088 · doi:10.5539/ass.v11n5p333

Tax Relieves: Costs of Their Application in Taxation and Issues of the Efficiency Evaluation

2015· article· en· W2118525088 on OpenAlexvenueno aff
Khaibat Magomedtagirovna Musaeva, Basir Khabibovich Aliev, Magomed Magomedovich Suleymanov, Tatiana Dyukina

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureRussian federationPublic economicsTax reformOrder (exchange)BusinessEconomicsEconomic policyPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

The main objective of this work was to develop a set of scientific and practical recommendations aimed atimproving the efficiency of preferential tax treatment based on the analysis of the costs of tax relieves, theirtypes, and their performance evaluation. Direct and indirect costs of tax relieves granted by the World Bank wereanalyzed within the research. Based on the analysis of legal acts that were in effect in 1990-2000, the specificfeatures and forms of tax relieves used in the Russian Federation were identified. The article shows the necessityof expanded understanding of the tax relieves based on their actual manifestations in various forms, and, basedon this, extension of the efficiency evaluation object in the circumstances of the Russian Federation. The articleformulates the conclusion about the need to transform the tax relieves in order to strengthen their stimulatingeffect on the economy development and achievement of the economic benefit and enhancement of the socialreturn. The regulatory-legislative, methodological, and organizational problems of evaluating the efficiency oftax advantages at the regional level (using the materials of the Republic of Dagestan) were revealed, and also aset of recommendations to address them was provided.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.125

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.295
Teacher spread0.245 · 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 teacher head, 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

Citations6
Published2015
Admission routes1
Has abstractyes

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