Bibliographic record
Abstract
The evil that men do lives after them; the good is oft interr'd with their bones – Shakespeare A set of new investigations into negativity biases in public opinion begins here, with an analysis of U.S. presidential evaluations, building directly on models of impression formation in the psychological literature discussed in Chapter 1. As past work in psychology suggests, negative domain-specific evaluations matter more to overall U.S. presidential assessments than do positive domain-specific evaluations. Analyses demonstrating this fact, which follow in this chapter, are partly a replication of past work, albeit with considerably more data and a somewhat different approach to modeling the asymmetry. But subsequent analyses then extend considerably what we know about political impression formation. First, comparative results make clear that the same dynamic is evident in other countries, supporting the notion that the negativity bias is not just a U.S. phenomenon. Subsequent analyses reveal heterogeneity in negativity biases as well. In short, they make clear that some people rely more strongly on negative information than do others. A final section then considers the difficulties in distinguishing “neutral” in interval-level measures – difficulties that make capturing the negativity bias difficult in some circumstances, and that point to the possibility that some past work finding a lack of evidence of a negativity bias may have been mistaken. Each of these issues is dealt with in turn throughout this chapter. Demonstrations rely on individual-level survey data drawn primarily from the American National Election Studies, but also from a series of Australian National Election Studies. In sum, results make clear the connection between work on impression formation in psychology and public attitudes toward political candidates. Moreover, they provide strong illustrations of a negativity bias in political behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".