Post-Truth Politics: The Effect of Reminders of Political Affiliation on Partisan Op-Ed Viewership
Bibliographic record
Abstract
Conventional economic theory depicts human-decision making as logical and rational. However, recent research has demonstrated that humans act as an irrational agent more often than not, and will habitually prioritize attitudes, emotions, values, and beliefs over a marginal analysis in their decision-making calculus. As such, individuals will regularly undertake actions in order to avoid conflicts with their beliefs. In particular, information contradicting an individual’s beliefs may be avoided to preserve an individual’s identity (information avoidance). This paper investigates the phenomena of belief-based utility and information avoidance in the political realm, an area in which the literature regarding the aforementioned theories are relatively sparse. Specifically, we explored whether a reminder of political affiliation could influence subjects to avoid reading op-eds possessing headlines indicative of a position commonly held by an opposing political party. The hypothesis was tested through a survey distributed on Amazon Mechanical Turk, where half the participants received a reminder while the other half did not. The results suggest a statistically significant relationship between reminders and media access behavior — a reminder can have a demonstrable effect on media access behavior by causing individuals to avoid op-eds that advocate for the viewpoints of a conflicting political party. This has multiple implications (increasing political polarization, expanding influence of private interest groups, etc.) regarding media viewership habits for the individual undertaking decisions that may deprive them of useful information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".