As the use of computational text analysis in the social sciences has increased, topic modeling has emerged as a popular method for identifying latent themes in textual data. Nevertheless, concerns have been raised regarding the validity of the results produced by this method, given that it is largely automated and inductive in nature, and the lack of clear guidelines for validating topic models has been identified by scholars as an area of concern. In response, we conducted a comprehensive systematic review of 789 studies that employ topic modeling. Our goal is to investigate whether the field is moving toward a common framework for validating these models. The findings of our review indicate a notable absence of standardized validation practices and a lack of convergence toward specific methods of validation. This gap may be attributed to the inherent incompatibility between the inductive, qualitative approach of topic modeling and the deductive, quantitative tradition that favors standardized validation. To address this, we advocate for incorporating qualitative validation approaches, emphasizing transparency and detailed reporting to improve the credibility of findings in computational social science research when using topic modeling.

Italy has often been implicitly or explicitly excluded from comparative political analyses due to its allegedly anomalous political arrangements and outcomes, but in more recent years, some of its once unusual experiences have come to seem as predictors of things to come in other countries. This contribution takes a closer look at such developments, starting with a consideration of the substantive differences between outliers and anomalies. It then presents and gives examples of four scenarios whereby changes might – or might not – have led Italy to converge with its neighbors. In sum, this essay contends that rather than viewing Italy as sui generis, it is fruitful to consider Italy and Italian politics as a kind of laboratory that not only incorporates all the basic elements of political dynamics but in which many relevant tendencies of current and prospective political and policy dynamics can be discerned.

This study aims to formulate a highly accurate numerical method, specifically a seventh-order Hermite technique with an error term of sixth order, to solve the Fisher and Burgers–Fisher equations. This technique employs a combination of orthogonal collocation on the finite element method and hepta Hermite basis functions. By ensuring continuity of the dependent variable and its first three derivatives across the entire solution domain, it achieves a remarkable level of accuracy and smoothness. The space discretization is handled through the application of hepta Hermite polynomials, while the time discretization is managed by the Crank–Nicholson scheme. The stability and convergence analysis of the scheme are discussed in detail. To validate the accuracy of the proposed technique, three examples are taken. The results obtained from these examples are thoroughly analysed and compared against the exact solutions and reliable data from the existing literature. It is established that the proposed technique is easy to implement and gives better results as compared with existing ones.

Official Ecuadorian gross domestic product (GDP) data begin in 1950. Prior, only preliminary estimates were available, based on very scattered evidence and broad assumptions. In this paper, we estimate new GDP figures for Ecuador for 1900–50. These are based on the quantitative and qualitative information available for the period, using extensive primary and secondary sources. The new data series allows analysing Ecuador’s economic growth and structural change and comparing them to industrialised core countries and other countries in the region. Unlike previous estimates, our series shows a sustained divergence of Ecuador from the core countries during the first half of the 20th century.

Recently, convergence liberals, such as Kevin Vallier, argue that the principle of social insurance could be publicly justified. Our paper challenges this marriage of convergence liberalism and welfare state. We begin by examining Vallier’s three reasons for the principle of social insurance: risk aversion, injustice and the promotion of political trust. We then argue that all these reasons are intelligibly objectionable. After examining five possible responses that convergence liberals may offer, this paper concludes that the principle of social insurance is not conclusively justified in the convergence conception of public justification.

FACTALS is a nonmetric common factor analysis model for multivariate data whose variables may be nominal, ordinal or interval. In FACTALS an Alternating Least Squares algorithm is utilized which is claimed to be monotonically convergent.

In this paper it is shown that this algorithm is based upon an erroneous assumption, namely that the least squares loss function (which is in this case a nonscale free loss function) can be transformed into a scalefree loss function. A consequence of this is that monotonical convergence of the algorithm can not be guaranteed.

It is shown that the psychometric test reliability, based on any true-score model with randomly sampled items and uncorrelated errors, converges to 1 as the test length goes to infinity, with probability 1, assuming some general regularity conditions. The asymptotic rate of convergence is given by the Spearman–Brown formula, and for this it is not needed that the items are parallel, or latent unidimensional, or even finite dimensional. Simulations with the 2-parameter logistic item response theory model reveal that the reliability of short multidimensional tests can be positively biased, meaning that applying the Spearman–Brown formula in these cases would lead to overprediction of the reliability that results from lengthening a test. However, test constructors of short tests generally aim for short tests that measure just one attribute, so that the bias problem may have little practical relevance. For short unidimensional tests under the 2-parameter logistic model reliability is almost unbiased, meaning that application of the Spearman–Brown formula in these cases of greater practical utility leads to predictions that are approximately unbiased.

This paper investigates the nexus between per capita income convergence and political institutions within the Eurozone. Employing data spanning the years 2002–2019, the research initially identifies multiple convergence clusters and subsequently examines the relationship between the creation of these clusters and different aspects of political institutions. The findings reveal that there are multiple steady states in the Eurozone, and their formation is notably influenced by political institutions alongside other conventional economic determinants derived from the Solow model. Furthermore, the study underscores that improvements in regulatory quality, as well as in aspects such as democracy, government effectiveness, and corruption control, positively impact income convergence across all member countries. These findings carry significant policy implications.

This article is concerned with the problem of determining an unknown source of non-potential, external time-dependent perturbations of an incompressible fluid from large-scale observations on the flow field. A relaxation-based approach is proposed for accomplishing this, which makes use of a nonlinear property of the equations of motions to asymptotically enslave small scales to large scales. In particular, an algorithm is introduced that systematically produces approximations of the flow field on the unobserved scales in order to generate an approximation to the unknown force; the process is then repeated to generate an improved approximation of the unobserved scales, and so on. A mathematical proof of convergence of this algorithm is established in the context of the two-dimensional Navier–Stokes equations with periodic boundary conditions under the assumption that the force belongs to the observational subspace of phase space; at each stage in the algorithm, it is shown that the model error, represented as the difference between the approximating and true force, asymptotically decreases to zero in a geometric fashion provided that sufficiently many scales are observed and certain parameters of the algorithm are appropriately tuned.