El Sistema de Detracciones y su impacto Tributario y Financiero en las Empresas del sector reparación e instalación de maquinaria y equipo, en el distrito de Miraflores, año 2018
Bibliographic record
Abstract
The present thesis investigation is done with the objective of determine the tax and financial impact of the Detraction System of the in the companies of the Repair & Installation of machinery and equipment sector in Miraflores, year 2018. In the first chapter are mentioned the most important definitions to comprehend the functioning of Detractions. Also, we identify fundamental necessities to argue our hypothesis y prove that Detractions influence in the tax and financial aspects of the companies. Finally, it is mentioned the importance of this sector in the Peruvian economy, its necessary to specify that this sector, Repair & Installation of machinery and equipment, it's a side activity, that belongs to the main sector, called Manufacturing Industry. In the second chapter are proposed the main and secondary problems, hypothesis and objectives. In the third chapter are mentioned the used methodologies. This are qualitative and quantitative. Represented by depth interviews and inquests, respectively. As population of the qualitative methodology, it has been made an interview to two experts in tax and accounting themes. In the quantitative methodology it has been made inquests to an established amount of companies located in Miraflores. In the fourth chapter, it's demonstrated the application of both instruments, qualitative and quantitative. Also, we develop the practical case that proves how Detractions influence in the financial statements of companies. In the end, the results obtained in the interviews, inquests and practical case are analyzed. These results complement one another with the conclusions which validate the main and specific hypothesis of the present study.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".