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Significant scholarly effort has been dedicated to defining the rule of law. The prevailing view in the literature is that the rule of law is a highly multidimensional and, as some suggest, an essentially contested concept. In this study, we employ advanced text-as-data methods, specifically diachronic word embeddings, to shed light on what the rule of law means and how its meaning has evolved over a century through parliamentary speeches in the UK and the US. We categorize the conceptualization of the rule of law into thin (procedural) and thick (substantive) definitions. Our findings indicate that procedural elements, such as rules and judiciary, maintain a strong and relatively more stable association with the rule of law. In contrast, substantive elements, which include rights and democratic principles, have become relatively less associated with the rule of law over time. Despite this decline, the rights component remains critically important to the concept, broadly equivalent in significance to procedural aspects. Because our analysis is confined to parliamentary debates from the UK and the US, the findings should be interpreted with caution when generalizing to other political contexts.

How do cabinet reshuffles affect the parliamentary opposition's use of no-confidence motions in the government? Opposition parties employ no-confidence motions as electoral signals to highlight government incompetence and to position themselves as a government in waiting. We argue that cabinet reshuffles – which prime ministers use to respond to policy failures, scandals, poor ministerial performance and disloyalty – present an opportunity for the opposition to deploy no-confidence motions to this end. The incentives to deploy this strategy, however, are contingent on the nature of the party system and are greatest where party-system concentration positions a single opposition party as the alternative to the government and sole beneficiary of a no-confidence vote. We test this expectation using a multilevel modelling approach applied to data on reshuffles in 316 governments and 16 parliamentary democracies, and find support for our expectation: cabinet reshuffles raise the probability of no-confidence motions conditional on party-system concentration.

Este artículo analiza la rotación de los gabinetes y los factores que inciden en la supervivencia de los 180 ministros en Chile entre 1990 y 2010. Con análisis de supervivencia, específicamente modelos de riesgos proporcionales y regresiones de Cox con fragilidad compartida, se evalúan características individuales de los ministros como predictores de supervivencia/riesgo y la influencia de los factores institucionales y eventos críticos. Se descubre que existen predictores individuales diferentes para los ministros del primer y segundo decenio del período, además se comprueba que eventos críticos como las crisis económicas y los escándalos de corrupción influyen en la supervivencia ministerial.

Resumen Este artículo analiza los principales factores que condicionan la selección de un directivo público de primer nivel jerárquico en Chile con base en una perspectiva de corte sociológico y el uso del concepto especies de capital. El trabajo realiza un recorrido teórico y descriptivo sobre la modernización del Estado y la profesionalización de la gestión pública en Chile. Posteriormente con una muestra de 45 casos correspondientes a altos directivos nombrados durante el período 2003-13, se trabaja con modelos de regresión logística para determinar los factores que condicionan la selección de estos funcionarios. Se llega a la conclusión que el capital político no es un factor que favorece la selección del actor. Abstract This article analyses the main factors that affect the election of a high civil servant in the first level of hierarchy in Chile, according to a sociological perspective using the capital species concept. This paper provides a theoretical and descriptive travel on State modernization and professionalization of public management in Chile. Later, with a sample of 45 cases regarding high civil servants designated during the period of 2003-13, a maximum likelihood logit models were performed to determine the factors that influence the selection of these civil servants. It concludes that political capital is not a factor that gives advantage in the selection. Resumo Este artigo analisa os principais fatores que influenciam a escolha de um diretivo público de primeiro nível hierárquico no Chile, com base numa perspectiva sociológica e no uso do conceito espécies de capital. A pesquisa realiza um rastreio teórico e descritivo sobre a modernização do Estado e a profissionalização da gestão pública no Chile. Logo, com uma amostra de 45 casos relativos aos altos funcionários públicos nomeados durante o período 2003-13, usaremos modelos de regressão logística para determinar os fatores que influenciam na seleção desses funcionários. Contudo, o artigo conclui que o capital político não é um fator que favorece a seleção do ator.

This article analyzes cabinet turnoverand factors that had an influence on the survival of 180 ministers in Chilebetween 1990 and 2010. With survival analysis, specifically, proportional hazards models and Cox regressions with shared-frailty, the individual characteristics of ministers, such as survival/risk predictors and the influence of institutional factors and critical events, are evaluated. It revealsthat there were different individual predictors for ministers during the first and second decade of the period, as well as the fact that critical events such as economic crises andcorruption scandals influenced the survival of ministers.

Large Language Models (LLMs) offer promising avenues for methodological and applied innovations in survey research by using synthetic respondents to emulate human answers and behaviour, potentially mitigating measurement and representation errors. However, the extent to which LLMs recover aggregate item distributions remains uncertain and downstream applications risk reproducing social stereotypes and biases inherited from training data. We evaluate the reliability of LLM-generated synthetic survey responses against ground-truth human responses from a Chilean public opinion probabilistic survey. Specifically, we benchmark 128 prompt-model-question triplets, generating 189,696 synthetic profiles, and pool performance metrics (i.e., accuracy, precision, recall, and F1-score) in a meta-analysis across 128 question-subsample pairs to test for biases along key sociodemographic dimensions. The evaluation spans OpenAI's GPT family and o-series reasoning models, as well as Llama and Qwen checkpoints. Three results stand out. First, synthetic responses achieve excellent performance on trust items (F1-score and accuracy > 0.90). Second, GPT-4o, GPT-4o-mini and Llama 4 Maverick perform comparably on this task. Third, synthetic-human alignment is highest among respondents aged 45-59. Overall, LLM-based synthetic samples approximate responses from a probabilistic sample, though with substantial item-level heterogeneity. Capturing the full nuance of public opinion remains challenging and requires careful calibration and additional distributional tests to ensure algorithmic fidelity and reduce errors.

This section analyzes the new challenges, analytical approaches, and methodologies for the study of different political elites. Based on the existing literature and the review of cases in Latin America, these articles reflect on the various research perspectives considering as dependent variables or phenomena to explain the governmental, legislative, and ministerial elites.

This article analyses the main factors that affect the election of a high civil servant in the first level of hierarchy in Chile, according to a sociological perspective using the capital species concept. This paper provides a theoretical and descriptive travel on State modernization and professionalization of public management in Chile. Later, with a sample of 45 cases regarding high civil servants designated during the period of 2003-13, a maximum likelihood logit models were performed to determine the factors that influence the selection of these civil servants. It concludes that political capital is not a factor that gives advantage in the selection.

The TextClass Benchmark project is an ongoing, continuous benchmarking process that aims to provide a comprehensive, fair, and dynamic evaluation of LLMs and transformers for text classification tasks. This evaluation spans various domains and languages in social sciences disciplines engaged in NLP and text-as-data approach. The leaderboards present performance metrics and relative ranking using a tailored Elo rating system. With each leaderboard cycle, novel models are added, fixed test sets can be replaced for unseen, equivalent data to test generalisation power, ratings are updated, and a Meta-Elo leaderboard combines and weights domain-specific leaderboards. This article presents the rationale and motivation behind the project, explains the Elo rating system in detail, and estimates Meta-Elo across different classification tasks in social science disciplines. We also present a snapshot of the first cycle of classification tasks on incivility data in Chinese, English, German and Russian. This ongoing benchmarking process includes not only additional languages such as Arabic, Hindi, and Spanish but also a classification of policy agenda topics, misinformation, among others.

This article benchmarked the ability of OpenAI's GPTs and a number of open-source LLMs to perform annotation tasks on political content. We used a novel protest event dataset comprising more than three million digital interactions and created a gold standard that includes ground-truth labels annotated by human coders about toxicity and incivility on social media. We included in our benchmark Google's Perspective algorithm, which, along with GPTs, was employed throughout their respective APIs while the open-source LLMs were deployed locally. The findings show that Perspective API using a laxer threshold, GPT-4o, and Nous Hermes 2 Mixtral outperform other LLM's zero-shot classification annotations. In addition, Nous Hermes 2 and Mistral OpenOrca, with a smaller number of parameters, are able to perform the task with high performance, being attractive options that could offer good trade-offs between performance, implementing costs and computing time. Ancillary findings using experiments setting different temperature levels show that although GPTs tend to show not only excellent computing time but also overall good levels of reliability, only open-source LLMs ensure full reproducibility in the annotation.