Book Review: <i>Negativity in Democratic Politics: Causes and Consequences</i> , by Stuart N. Soroka
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".