Book Review: <i>Negativity in Democratic Politics: Causes and Consequences</i> , by Stuart N. Soroka
Notice bibliographique
Résumé
Negativity in Democratic Politics: Causes and Consequences. Stuart N. Soroka. New York: Cambridge University Press, 2014. 180 pp. $27.99 pbk.As you most likely will prioritize the negative over the positive, let us start with some of the critiques of Negativity in Democratic Politics: It is pretty short (too short?), some evidence presented in the book needs to be bolstered by future research, and it ignores some related literature streams. But, quite frankly, its confined focus is also among the strengths of this interesting work by Stuart N. Soroka, a professor of communication studies and political science at the University of Michigan.He compiled a large number of studies focusing on the role of negativity in political processes from a variety of disciplines (political science, psychology, economics, communications, biology, and physiology). This interdisciplinary approach allows scholars with an interest in this topic to peek across the borders between their discipline and many others. This book reveals that researchers with varying backgrounds have studied negativity from different angles. As the relations between those studies are not always evident, Soroka attempts to connect them to complete a larger puzzle.As a result, we see the bigger picture. Yet this book also reveals that some pieces are still missing. In some cases, those pieces may be found in other literature focusing on attribution bias and evolutionary biology, but in other cases, those pieces still need to be created-and this book could serve as a stepping stone for such research endeavors. Soroka also presents a variety of his own research to fill some of those holes.The book is not a direct protest against the emphasis on negative information in the political sphere. As mentioned several times, negative information is important for citizens to monitor their communities, especially for holding political officials accountable. Instead, the book presents a variety of examples of how negative information has a greater effect on judgments than positive information.Much of the research discussed has been conducted in the United States, although comparable results from other countries are provided, primarily Canada and the United Kingdom-which, the author acknowledges, are not wildly different cultures, but they are certainly not homogeneous in all aspects either. Soroka argues that this indicates that negativity may not solely be a cultural phenomenon in a specific country, but perhaps biological-although he did not delve much into the latter type of literature to strengthen his case. …
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».