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Record W7064890425

Comparing the Influence of True Information and False Information on Climate Change Policy Preferences

2018· article· en· W7064890425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArizona State University Library Digital Repository (Arizona State University) · 2018
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Quarter (Canadian coin)Public policyPoliticsSubject (documents)Public opinion
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.011
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.158
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it