Letteratura scientifica selezionata sul tema "Abstention mechanisms"

Invalid voting and absenteeism are alternative sources of abstention under compulsory voting. Previous research failed to systematically study the mechanisms behind each form of non-voting and the relationships between them. We develop an analytical framework and an empirical strategy to jointly examine invalid voting and absenteeism in Brazil, the world’s largest democracy with mandatory voting. Using Bayesian inferential methods and analyzing both individual and district-level data, we show that less educated and politically knowledgeable citizens are less likely to vote and, when they do, they are typically unable to successfully complete their ballot. Unlike absenteeism, invalid voting also has a political dimension reflecting voters’ disenchantment with elections and democratic performance. Both sources of abstention coexist and, together, undermine electoral participation.

abstract: When and how do voters punish politicians for subverting democracy? To investigate the role of the public in democratic backsliding, I develop a conceptual framework that differentiates among three mechanisms: vote switching, backlash, and disengagement. The first mechanism entails defection by voters from a candidate who undermines democracy to one who does not; the latter two mechanisms entail transitions between voting and abstention. I estimate the magnitude of each mechanism by combining evidence from a series of original survey experiments, traditional surveys, and a quasi-experiment afforded by the rerun of the 2019 Istanbul mayoral election, in which the governing party, akp, attempted to overturn the result of an election that it had lost. I find that although vote switching and backlash contributed to the akp's eventual defeat the most, each of the three mechanisms served as a democratic check in some subset of the Istanbul electorate. Persuasion, mobilization, and even demobilization are all viable tools for curbing the authoritarian tendencies of elected politicians.

Alcohol consumption ranging from 1–2 drinks/day associates with a lower risk of coronary heart disease in some studies. The underlying mechanisms are unclear. The Metabolic Imprints of Alcoholic Beverages (MetAl) trial aimed to explore the short-term effects of moderate alcohol consumption on cardiovascular biomarkers. A 2 × 3-week cross-over single-blinded intervention trial investigating the effect of 1–2 drinks/day (~12–24 g) compared with abstention on 1H Nuclear Magnetic Resonance-measured main lipoproteins and subfractions was performed in 26 healthy adults. Volunteers were classified as occasional or habitual drinkers based on their habitual alcohol intakes (<2 or ≥2 drinks/week). Compared with abstention, 1–2 drinks/day increased HDL2a-C (p = 0.004), HDL3-C (p = 0.008), and HDL non-significantly (p = 0.19). Total apoA1 and apoA1 in HDL and its subfractions increased (p < 0.05). Novel findings were a decreased apoB/apoA1 ratio (p = 0.02), and increased HDL2a phospholipid content (p = 0.04). In women alone, the results were similar but attenuated, and LDL-P decreased. Thus, changes in apoA1- and HDL-related biomarkers occur within weeks in moderate drinkers. Compared with abstention, 1–2 drinks/day increased total apoA1 more strongly than HDL-C and increased the cholesterol, apoA1, and phospholipid content of several HDL subfractions. Whether this provides a cardiovascular benefit requires further study. Clinicaltrials.gov: NCT03384147.

The effects of alcohol on cardiovascular health are heterogeneous and vary according to consumption dose and pattern. These effects have classically been described as having a J-shaped curve, in which low-to-moderate consumption is associated with less risk than lifetime abstention, and heavy drinkers show the highest risk. Nonetheless, the beneficial effects of alcohol have been questioned due to the difficulties in establishing a safe drinking threshold. This review focuses on the association between alcohol consumption and cardiovascular risk factors and the underlying mechanisms of damage, with review of the literature from the last 10 years.

Is electoral behaviour affected by the current challenges of the EU and, if it is, through which channels and mechanisms? This study offers a cross-national analysis together with a broad understanding of both the crisis phenomenon and electoral behaviour. To investigate this research question appropriately, we first distinguish at the most general level between the two main behavioural alternatives at play when it comes to electoral behaviour, namely abstention and vote choice. Second, and no less important, we differentiate between the mechanisms that mediate the relationship between the ‘EU under stress’ and electoral behaviour, namely egocentric and sociotropic economic voting motivations. Drawing on data from the European Election Study 2014, our article provides important insights into the study of electoral behaviour in an EU under stress. First, we are able to show that the multiple crises that have hit the EU have the potential to determine both turnout and the decision to vote for a Eurosceptic party. Second, different mechanisms are in play for each of the two behavioural alternatives: Turnout is clearly related to egocentric determinants and thus depends on individuals’ personal exposure to the financial crisis. Conversely, the decision to vote for a Eurosceptic party is based on a different mechanism. Voters—without necessarily being personally affected by the crisis—have a higher propensity to vote for a Eurosceptic party if they perceive their country to be threatened by such an EU under stress. These findings add to a better understanding of the EU’s multi-level democracy.

Abstract Despite the interest in the role of consumption in the bereavement process, the concept of consumer grief and the process consumers experience when grieving remain undertheorized. This article aims to conceptualize consumer grief considering the disruption brought by the COVID-19 pandemic and the need to understand how consumers respond to this scenario, understanding the mechanisms consumers create to deal with loss. In view of the importance of extraordinary experiences due to their embodied, social, and transformational power, in this netnographic study, we explore marathon runners’ loss of an extraordinary experience. Consumers deal with the loss of an experience through a process composed of five mechanisms mediated by social media, which enable consumers to reverse, reframe, and reestablish the experience. The mechanisms of refutation, despair, abstention-compensation, transgression, and acceptance show how consumers behave in different moments of grief, allowing them to build their trajectories in the grieving process, individually and collectively. As a contribution, we expand the literature on consumer grief by focusing on the specific concept of consumer grief, explaining the processes consumers go through when they deal with the loss of an experience. Additionally, we present a collective perspective on the grieving process, shifting the analysis of the grief of an individual or a family unit to the socialization of grief.

In the past 25 years, differentiated policies and mechanisms of differentiated decision-making have rapidly gained prominence and relevance on EU level. It is not only well-known EU policies such as the euro currency or the Schengen area of borderless travel which fall within the scope of EU activity in which not all Member States participate in. Rather, also lesser known instruments exist, such as constructive abstention in Common Foreign and Security Policy, the enhanced cooperation procedure, or Permanent Structured Cooperation (PESCO). A rapidly less homogenous circle of EU members and either the unwillingness or the unpreparedness on part of some of them to fully participate - if at all - in certain EU activity has resulted in an ever more differentiated EU. Is the Union destined to become one of ‘bits and pieces’? Or is there a solution to the conundrum of the incompatibility of simultaneously enlarging and deepening the EU, an incompatibility which has been one of the driving factors behind differentiations? In other words: Do strategies exist to eventually truly create the ever closer Union of fully integrated Member States?

This article explores the ethical imperative to dramatise in the work of Georges Bataille and Gilles Deleuze, two of the most radical thinkers in twentieth-century philosophy, as a peculiar kind of askesis. Whereas askesis is often associated with asceticism or self-denial, in the sense of self-regulation and abstention, Bataille and Deleuze advocate training the self towards intensification of the liminal and extreme (disruption rather than composure), which can rather be understood as a denial of self – its dissolution or laceration. Few attempts have been made to compare their work, even though both share a commitment to resisting the closures that bind our desires and inhibit our full participation in and confrontation with the ebbs and flows of an impersonal, immanent life. Through careful consideration and comparison of their work, I argue that both offer important methods for engendering modalities of ecstatic being characterised by sensitivity to immanence, which have important ramifications for our ability to address phenomena of ethical indifference and resist the constrictions of social control mechanisms that decimate our political imaginations and inhibit our resolve to invent a different future. In the final sections, I interrogate the differences in their invocation of affect and art.

One of the major unresolved questions in influenza A virus (IAV) ecology is exemplified by the apparent disappearance of highly pathogenic (HP) H5N1, H5N2, and H5N8 (H5Nx) viruses containing the Eurasian hemagglutinin 2.3.4.4 clade from wild bird populations in North America. The introduction of Eurasian lineage HP H5 clade 2.3.4.4 H5N8 IAV and subsequent reassortment with low-pathogenic H?N2 and H?N1 North American wild bird-origin IAVs in late 2014 resulted in widespread HP H5Nx IAV infections and outbreaks in poultry and wild birds across two-thirds of North America starting in November 2014 and continuing through June 2015. Although the stamping out strategies adopted by the poultry industry and animal health authorities in Canada and the United States—which included culling, quarantining, increased biosecurity, and abstention from vaccine use—were successful in eradicating the HP H5Nx viruses from poultry, these activities do not explain the apparent disappearance of these viruses from migratory waterfowl. Here we examine current and historical aquatic bird IAV surveillance and outbreaks of HP H5Nx in poultry in the United States and Canada, providing additional evidence of unresolved mechanisms that restrict the emergence and perpetuation of HP avian influenza viruses in these natural reservoirs.

The recent development in formal studies of elections produced two sets of findings that question the custom to treat voter information as a prerequisite for competent democratic decision-making. One argues that uninformed abstention is an effective strategy to approximate informed electoral outcome, and another suggests that uninformed voters may motivate strategic political elites to improve accountability. This article bridges and extends these two findings by analyzing strategic incentives in the comprehensive voting model with abstention and its connection with electoral accountability. The proposed model offers a contextual explanation for two contrasting logic in uninformed abstention, delegation and discouragement, and shows that uninformed voting with abstention sometimes improves accountability. Furthermore, uninformed abstention is more effective in generating democratically preferred outcome under delegatory than discouraged context. The results make a significant addition to the existing accountability literature by providing a more general mechanism by which less voter information improves policy outcomes.

Les réseaux neuronaux ont fait preuve d'avancées remarquables dans des tâches réputées difficiles, comme la classification automatique d'images ou le traitement du langage naturel. Toutefois, leur nature de boîte noire limite leur explicabilité et entrave leur capacité à exploiter des connaissances extérieures aux données. Cette thèse a pour but d'explorer et de proposer des techniques d'intégration des connaissances de haut niveau dans les réseaux neuronaux afin d'améliorer les performances et l'interprétabilité. La première partie de la thèse est centrée sur l'intégration de connaissances aux données d'entrée d'un réseau. Son premier chapitre s'adresse à la préparation des données. On y propose une formalisation du prétraitement afin de garantir la transparence et la reproductibilité de cette étape. Cette formalisation nous permet d'étudier l'impact de la data-augmentation : pour caractériser ce qu'est une bonne préparation des données, et l'état informatif d'un dataset, un ensemble de mesures et de principes est proposé, ensuite des protocoles expérimentaux sont conçus afin d'évaluer ces principes sur le dataset BreakHis. Le deuxième chapitre de cette partie s'adresse à l'exploitation de connaissances haut-niveau pour l'établissement d'un ordre de présentation des données au réseau. Nous introduisons l'apprentissage par curriculum incrémental sur l'ordre de passage des données en entrée. Les résultats obtenus améliorent l'exactitude et la vitesse de convergence. Bien que cette étude soit menée sur le dataset BreakHis, nous pensons qu'elle est généralisable à n'importe quel autre dataset. La deuxième partie est centrée sur l'intégration de connaissances au sein de l'architecture du réseau et au niveau de sa sortie. Dans ce cadre, nous nous sommes intéressés à la classification multi-label hiérarchique, pour laquelle nous avons formalisé les connaissances représentant le lien hiérarchique. Pour cela nous avons introduit deux contraintes : l'une représentant le fait qu'un objet ne peut être affecté qu'à une seule classe à un niveau donné de la hiérarchie, et l'autre imposant que l'affectation globale d'un objet respecte la hiérarchie de classe (par exemple, on interdit de classer un élément comme abeille pour son sous-type et mammifère pour son sur-type). Nous avons conçu une architecture et une fonction de perte qui imposent ces deux contraintes durant l'apprentissage. L'architecture se distingue de l'état de l'art par le fait qu'un seul réseau est utilisé pour prédire simultanément les labels des différents niveaux : toutes les couches sont responsables de la prédiction du n-uplet des classes. Plusieurs variantes du réseau ont été expérimentées sur cinq jeux de données différents et les résultats confirment l'efficacité des contraintes hiérarchiques soutenant ainsi l'importance de la prise en compte de connaissances externes. Afin de raffiner les résultats de cette classification hiérarchique, nous avons introduit un mécanisme d'abstention, sous forme d'une troisième contrainte poussant le réseau à donner une prédiction au niveau de spécificité le plus précis possible sur lequel sa confiance est suffisante et s'abstenir sinon. Nous avons défini différents seuils de confiance et proposé différentes contraintes sur les seuils relativement à la hiérarchie des classes. Pour évaluer ce mécanisme, de nouvelles métriques de classification prenant en compte l'abstention ont été définies. Nous avons mené des expérimentations sur les mêmes jeux de données et les résultats ont montré l'intérêt de l'abstention, et la nécessité de définir un seuil empirique adapté à chaque dataset. Pour conclure, les travaux de cette thèse soulignent l'intérêt d'exploiter des connaissances externes dans le domaine des réseaux de neurones ceci au niveau des trois composantes de ce système d'apprentissage : en entrée pendant la préparation des données, dans la structure du réseau, et à la sortie lors de la prise de décision de classification
Neural networks have made remarkable improvements in challenging tasks such as automatic image classification and natural language processing. However, their black-box nature hinders explainability and limits their ability to leverage external knowledge. The purpose of this thesis is to explore and propose techniques for integrating knowledge into neural networks in order to improve their performance and interpretability. The first part of the thesis focuses on integrating knowledge at the input level. The first chapter deals with data preparation. A formalization of pre-processing is proposed to ensure the transparency and reproducibility of this step. This formalization enables us to study the impact of data augmentation: to characterize a good data preparation, and the informative state of a dataset, a set of measures and principles is proposed, then experimental protocols are designed to evaluate these principles on the BreakHis dataset. The second chapter of this part focuses on exploiting high-level knowledge to determine the order in which data should be inserted into the network. We introduce an incremental curriculum learning for ordering the input data. The results obtained show an improvement of accuracy and convergence speed. Although this study is carried out on the BreakHis dataset, we believe that it can be generalized to any other dataset. The second part is devoted to the integration of knowledge within the network architecture and at the output level. In this context, we focus on hierarchical multi-label classification, for which we formalize the knowledge representing the hierarchical link. For this aim, we introduce two constraints: one representing the fact that an object can only be assigned to one class at a given level of the hierarchy, and the other imposing that the global assignment of an object respects the class hierarchy (for example, we forbid classifying an element as a bee for its sub-type and a mammal for its super-type). We design an architecture and a loss function that impose these two constraints during learning. The architecture differs from the state of the art in that a single network is used to simultaneously predict the labels of the different levels: all layers are responsible for predicting the tuple of classes. Several variants of the network have been tested on five different datasets and the results confirm the efficiency of the hierarchical constraints, thus supporting the importance of taking external knowledge into account. In order to refine the results of this hierarchical classification, we introduce an abstention mechanism, in the form of a third constraint that enforces the network to give a prediction at the most precise level of specificity on which its confidence is sufficient and to abstain otherwise. We define different confidence thresholds and proposed different constraints on the thresholds accordingly to the class hierarchy. To evaluate this mechanism, new classification metrics that take abstention into account are defined. We carry out experiments on the same five datasets and the results show the interest of abstention, and the need to define empirical thresholds adapted to each dataset. In conclusion, the work in this thesis highlights the value of exploiting external knowledge, this is true for the three main components of a neural network: at the input level during data preparation, in the structure of the network, and at the output level when classification decisions are made

Antibiotic resistance has become a serious worldwide concern that endangers the environment and public health. This problem has led to protracted illnesses, higher healthcare expenses, and higher death rates. It is caused by the abuse and overuse of antibiotics. This leads to extended hospital stays, escalated medical expenses, and heightened vulnerability, especially among marginalized communities with limited access to healthcare. Prolonged illnesses due to resistant infections, which can lead to increased hindering of human capital development. These resistant strains can contaminate water bodies, soil, and food, subsequently entering the human food chain and exacerbating the spread of resistance. In conclusion, antibiotic/antimicrobial abstention stands as a menacing global predicament, entwining social health and environmental stability. Immediate and coordinated efforts are imperative to preserve the efficacy of antibiotics, safeguard human well-being, and protect the environment from the far-reaching repercussions of antibiotic resistance.