Comparing the Influence of True Information and False Information on Climate Change Policy Preferences
Bibliographic record
Abstract
abstract: This past summer, Pew Research Center conducted a ten-question survey to test Americans' knowledge on current events. Questions ranged from how Zika virus is transmitted, to the name of the current president of France. A majority of the participants were unable to answer half of the questions correctly (Pew Research Center, 2017). While previous Pew knowledge surveys saw a majority of Americans answer only one quarter of the questions correctly (2014), it is clear that Americans today are still not completely up-to-date on current affairs. Along with Americans lacking knowledge of current affairs, the recent election saw the rise in accusations of "fake news." These calls inspired me to undertake my thesis project to try to answer the question: "does fake news actually impact the public's policy preferences, and if so, by how much?" While studies have been conducted to test the relationship between policy misperceptions and policy preferences, there have not been many studies released to directly test the impact of incorrect information on policy preferences. The underlying purpose of this study is to test how introduction of new information, particularly falsehoods, influences policy preferences. Specifically, I focus on policy preferences related to anthropogenic climate change . Any valid research that seeks to analyze the effect of political information on policy preferences needs to starts by discovering how much the public knows about the particular policy issue that the researcher is focusing on. Without explicitly saying as much, all of the research on the subject that I have read has come to the same conclusion: American's are indeed politically unaware on a wide array of issues. The areas of policy that Americans lack knowledge on are widespread: education (Howell and West, 2009), welfare (Gilens, 2001), the war in Iraq (Berinsky, 2007; Kull, 2003), and facts about political candidates (Nyhan and Reifler, 2012) are just some of the issues that Americans seem to know little about. Literature discussed in the following section shows how researchers have tried to understand how policy knowledge impacts policy opinions. Researchers primarily collected their data either one of two ways: by analyzing existing survey data or by conducting their own survey.
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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 | 0.040 | 0.291 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".