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Record W2952260393

Evolución del interés por mora en el sistema impositivo ecuatoriano de enero del 2003 a junio

2011· dissertation· es· W2952260393 on OpenAlexaboutno aff
Valenzuela Luna, Javier Armando

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ArrearsGeographyHumanitiesDemographyArtEconomicsArchaeologySociologyPayment
DOInot available

Abstract

fetched live from OpenAlex

This research work was developed in the Republic of Ecuador, located in the northwestern part of South America's main objective was to analyze the evolution of interest for late tax system Ecuadorian January 2003 to June 2011. The results were as follows: the first quarter of 2008 found the highest rate of interest for calculating tax obligations past due amounts to 1.340% and the lowest in the first quarter of 2005 to 0.736%. The biggest change for the second quarter was in 2008 with 1.304% and the lowest in 2006 with 0.816%. The year 2008 recorded the highest interest rate to 1199% and lowest in 2006 with 0.780%. In the fourth quarter, the largest change occurred in 2008 with 1.164% and the lowest in 2005 with 0.712%. With respect to the quarterly change in interest arrears, provided that in the first quarter of 2004 there was a decrease of -20.2% in proportion to the first quarter of 2005, but in 2008 there was an increase of 26, 0% all this in relation to the law passed that year with affinity to taxation, in 2005 there was an increase of 10.8% in relation to the interest of the second quarter compared to last year, also indicates that the highest decrease was seen in 2003 with -7.5%. in the third quarter of the years under study shows a decreasing percentage of interest in arrears, so much so that in the year 2008 decreased by 8.8% considered the lowest value, unlike in 2009 that there was no variation (0, 00%). establishing research consolidates the year with lower interest rates is 2008, this phenomenon is that for this period is changed proportional calculation factor from 1.1 to 1.5.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.233
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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