The Multidimensional Structure of Risk: How Dread and Control Shape Perceptions Toward Artificial Intelligence.
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.
Bibliographic record
Abstract
Studies of public opinion about new and emerging technologies are gaining momentum (Scheufele & Lewenstein, 2005; Cobb, 2005; Druckman & Bolsen, 2011; Zhang & Dafoe, 2019). At the centre of this emerging research agenda is a focus on people’s technological readiness (i.e., Liljander et al., 2006; Mankins, 2009) and evaluation of perceived risks (i.e., Macoubrie, 2004; Priest et al., 2010; Gallego et al., 2022). More recently, scholars have attempted to understand how one’s judgement about the seriousness or pervasiveness of new technologies impacts public acceptance (Renn & Benighaus, 2013), particularly given the salience of ChatGPT, which has raised concerns about academic integrity, personal security (Lund & Wang, 2023), and the spread of misinformation (Hsu & Thompson, 2023). Previous work suggests that individual risk evaluations have become increasingly multidimensional (Nelkin, 1989; Wildavsky & Dake, 1990; Cobb, 2005; Renn & Benighaus, 2013), with beliefs about familiarity and the technology’s degree of danger often serving as primary concerns. However, two recently overlooked dimensions with important theoretical implications for opinions about the extent to which new technologies should get adopted in society include perceived dread and control (Slovic, 1987). Cobb (2005) summarises these two dimensions as beliefs about the perceived magnitude of the risk posed by the new technology (i.e., dread) and its controllability, which refers to the perceived capacity to control the growth and outcome of the technology. Consequently, we leverage original data and a survey experiment fielded in Canada and Japan - the former a significantly understudied context for investigations of attitudes toward technology (Besley, 2013), to examine the following questions: • What is the nature of perceptions of dread and controllability concerns regarding artificial intelligence (A.I.) technology in Canada and Japan? • Who is most susceptible to beliefs about dread and controllability concerns posed by A.I. technology in these contexts? • How do frames showing varying degrees of the perceived magnitude and controllability of technological risks impact public opinion about adopting A.I.-based technology in society? And, does it vary by topic?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it