Impacto económico-financiero de la contribución pagada por la concentración de mercado creada por la Ley Orgánica de Telecomunicaciones en la empresa Directv Ecuador C. Ltda en el año 2016.
Bibliographic record
Abstract
In this work was discussed the economic 0061nd financial impact of the contribution paid by market concentration created by “Ley Orgánica de Telecomunicaciones” in the company in 2016, for this research information has been obtained until the third quarter of 2017. \nThe purpose of this work is to know the effects and consequences that were generated for Directv Ecuador because it is the only corporation that had to pay this contribution within the companies of television by subscription or paid television. In order to achieve this purpose was made an analysis about the internal treatment payment of the contribution for market share during the years 2015, 2016 and 2017. \nTherefore, information was taken from primary sources; such as “Reglamento a la Ley Orgánica de Telecomunicaciones”, “Reglamento Pago por Concentración de Mercado para Promover la Competencia” and documents of the company. In addition, secondary sources in order to obtain clear, accurate and truthful information. \nAs a result, it was concluded that in 2016 the payment of the contribution by market share was assumed totally by the Directv, so this payment was considered an representative amount for it..
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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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".