Evolución del interés por mora en el sistema impositivo ecuatoriano de enero del 2003 a junio
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".