Description: We assess whether the classic psychometric paradigm of risk perception can be improved or
supplanted by novel approaches relying on language embeddings. To this end, we introduce
the Basel Risk Norms, a large data set covering 1,004 distinct sources of risk (e.g., vaccination,
nuclear energy, artificial intelligence) and compare the psychometric paradigm against novel
text and free-association embeddings in predicting risk perception. We find that an ensemble
model combining text and free association rivals the predictive accuracy of the psychomet-
ric paradigm, captures additional aspects not accounted for by the classic approach, and has
greater range of applicability to real-world text data, such as news headlines. Overall, our
results establish the ensemble of text and free-association embeddings as a promising new tool
for researchers and policymakers to track real-world risk perception.
i think the 'psychometric' part we can use but perhaps not the novel part. perhaps a complex one.
https://osf.io/gu9df/
License: CC-By Attribution 4.0 International
note the following:
i think the 'psychometric' part we can use but perhaps not the novel part. perhaps a complex one.