Tarifas telefónicas móviles y su incidencia en el costo de vida de los colombianos
Bibliographic record
Abstract
ResumenEl proposito del presente trabajo es analizar el impacto financiero de las tarifas telefonicas moviles, en el costo de vida de los colombianos y compararla empresa internacional contra la nacional. La muestra estuvo representada por 7 paises, entre ellos: Mexico, Argentina, Espana, Estados Unidos, Canada, Australia y Colombia; los cuales fueron seleccionados por ser los mas representativos de este sector empresarial y por similitudes socioeconomicas. Se emplearon tecnicas estadisticas como la media, varianza, desviacion estandar y coeficiente de variacion para medir de manera efectiva el efecto en la economia. Los resultados obtenidos permitieron evidenciar que las empresas operadoras de servicios moviles manejan diferentes tarifas y servicios, siendo Estados Unidos el pais con las tarifas mensuales mas bajas y Canada la que posee la tarifa mensual mas alta, Mexico con tarifas intermedias y Colombia pese a que cuenta con los salarios minimos mas bajos, sus tarifas mensuales son altas comparadas con los paises estudiados. Se concluye que estas tarifas influyen de manera directa en la finanzas de las familias y que los paises con mayor impacto en la economia personal son: Mexico y Colombia respectivamente.Palabras Clave: Telefonia, Movil, Tarifas, Impacto,Financiero, Servicios, competitividad, cobertura.AbstractThe purpose of this paper is to analyze the financial impact of mobile telephone rates on the cost of living of Colombians and compare the international versus the national company. The sample was represented by 7 countries, among them: Mexico, Argentina, Spain, the United States, Canada, Australia and Colombia; which were selected for being the most representative of this business sector and for socioeconomic similarities. Statistical techniques were used such as the mean, variance, standard deviation and coefficient of variation to effectively measure the effect on the economy. The results obtained showed that mobile service operators handle different tariffs and services, with the United States being the country with the lowest monthly rates and Canada having the highest monthly rate, Mexico with intermediate rates and Colombia, despite having an with the lowest minimum wages, their monthlyrates are high compared to the countries studied. It is concluded that these rates directly influence the finances of families and that the countries withthe greatest impact on the personal economy are: Mexico and Colombia respectively.Keywords: Mobile, telephony, Charge, Finances, services, coverage, competitiveness, impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".