Media Systems in the Digital Age: An Empirical Comparison of 30 Countries

Media systems have changed significantly as a result of the development of information technologies. However, typologies of media systems that incorporate aspects of digitalization are rare. This study fills this gap by identifying, operationalizing, and measuring indicators of media systems in the digital age. We build on previous work, extend it with new indicators that reflect changing conditions (such as online news use), and include media freedom indicators. We include 30 countries in our study and use cluster analysis to identify three clusters of media systems. Two of these clusters correspond to the media system models described by Hallin and Mancini, namely the democratic-corporatist and the polarized-pluralist model. However, the liberal model as described by Hallin and Mancini has vanished; instead, we find empirical evidence of a new cluster that we call “hybrid”: it is positioned in between the poles of the media-supportive democratic-corporatist and the polarized-pluralist clusters.

An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade

Classification problems are common activities in many different domains and supervised learning algorithms have shown great promise in these areas. The classification of goods in international trade in Brazil represents a real challenge due to the complexity involved in assigning the correct category codes to a good, especially considering the tax penalties and legal implications of a misclassification. This work focuses on the training process of a classifier based on bidirectional encoder representations from transformers (BERT) for tax classification of goods with MCN codes which are the official classification system for import and export products in Brazil. In particular, this article presents results from using a specific Portuguese-language-pretrained BERT model, as well as results from using a multilingual-pretrained BERT model. Experimental results show that Portuguese model had a slightly better performance than the multilingual model, achieving an MCC 0.8491, and confirms that the classifiers could be used to improve specialists’ performance in the classification of goods.

The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets

Any quantitative method is shaped by certain rules or assumptions which constitute its own rationale. It is not by chance that these assumptions determine the conditions and constraints which permit the evidence to be constructed. In this article, we argue why the Regression Tree Method’s rationale is more suitable than General Linear Model to analyze complex educational datasets. Furthermore, we apply the CART algorithm of Regression Tree Method and the Multiple Linear Regression in a model with 53 predictors, taking as outcome the students’ scores in reading of the 2011’s edition of the National Exam of Upper Secondary Education (ENEM; N = 3,670,089), which is a complex educational dataset. This empirical comparison illustrates how the Regression Tree Method is better suitable than General Linear Model for furnishing evidence about non-linear relationships, as well as, to deal with nominal variables with many categories and ordinal variables. We conclude that the Regression Tree Method constructs better evidence about the relationships between the predictors and the outcome in complex datasets.

BHEP Class of Tests for Multivariate Normality: An Empirical Comparison

This paper compares the empirical power performances of eight tests for multivariate normality classified under Baringhaus-Henze-Epps-Pulley (BHEP) class of tests. The tests are compared under eight different alternative distributions. The result shows that the eight statistics have good control over type-I-error. Also, some tests are more sensitive to distributional differences with respect to their power performances than others. Also, some tests are generally more powerful than others. The generally most powerful ones are therefore recommended.

Dynastic Potential Crossover Operator

An optimal recombination operator for two parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this paper, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.

Making Teaching and Learning Effective Using Analytics

In this paper, an empirical comparison of three state-of-the-art classifier methods (artificial immune recognition systems, Lazy-K Star, and random tree) to predict teachers’ ability to adapt in a classroom environment is carried out. Two educational databases are used for this task. First, measures collected in an academic context, especially from classroom visits, are used (database 1). Then, the three classifiers quantify the acts, behaviors, and characteristics of teaching effectiveness and the teacher’s “ability to adapt in the classrooms.” Professional classrooms visits to more than 200 teachers are used as the second database (database 2). An interactive grid gathering 63 educational acts and behaviors is conceived as an observation instrument for those visits. Within the Waikato Environment for Knowledge Analysis library environment, and with the progressive enhancement of the raw database, the utilization of state-of-the-art classification methods when predicting teaching effectiveness shows promising results, especially when data quality issues are considered.