Countering the Politicization of Science Through Value-Congruent Messengers
Why this research is important
The recent COVID-19 pandemic and climate change have shown that the biased assimilation of scientific information is negatively impacting our collective ability to converge on factually accurate beliefs critical for undertaking coordinated action in the face of societal threats.
Scientists are often de facto communicators about dangers to public health and wellbeing, but as recent events have shown, when it comes to controversial issues the public does not necessarily view them as neutral or above the political fray.
Making matters worse, science literacy and comprehension do not necessarily predict belief on issues, which have become markers of social identity and belonging. [1] Danger is real, but risk is socially constructed, contextual, and highly influenced by who assesses and communicates the risk. Given the evolutionary importance of in-group acceptance for survival, a rational individual’s (sub-conscious) risk calculations naturally include the potential cost of adopting beliefs incongruent with one’s identity or close social ties.
Biased assimilation of information (a form of motivated reasoning) serves to protect individuals against potential threats to their identity and social affiliations. In some cases, the benefits of holding factually accurate views may be outweighed by potential threats to social identity, reputation, and a sense of belonging. Science curiosity (the general disposition, variable in intensity across persons, that reflect the motivation to seek out and consume scientific information for personal pleasure) has been found to be an effective antidote for countering these destructive forces [2].
More, a growing body of work suggests that embedding information in story structure, rather than the analytical frames typically used by science communicators, more effectively triggers emotion, a critical impetus for action-taking in the face of threats [3]. One of the main mechanisms by which stories exert their influence is through identifiable characters thought to share important values and goals.
This research project would connect largely disconnected streams of research to advance our understanding of how scientific information is shared as social information via the perceived values of the messenger, interrogating the following questions:
Does perceived value congruence between messenger and receiver influence perception of future pandemic risk and willingness to take action in the face of threats? Is there a relationship between science curiosity and post-narrative influence?
H1: We hypothesize that to the degree there is value congruence between risk messenger and message receiver, this will result in higher post-narrative influence in the form of risk perception and willingness to take action in the face of a threat.
H2: Moreover, we hypothesize that post-narrative influence predicted in H1 is mediated by counterarguing and identification.
H3: We expect there will be a positive correlation between science curiosity and post-narrative influence.
Methodology
We will test our hypotheses using a series of three online experiments with 2x2 factorial designs (perceived messenger/receiver value match vs. mismatch) and identical protocols.
Key variables:
IV: Messenger values (operationalized as religiosity (study 1), Group- (Individualism vs. Communitarian) (study 2) and Grid (Hierarchical vs. Egalitarian) (study 3) cultural worldviews
DV1: Risk perception (RP)
DV2: Willingness to take action, operationalized as future Vaccine Acceptance (VA)
DV3: Science Curiosity (SC)
Mediators: M1: Counterarguing (CA); M2: Identification (ID).
Moderator: MO: Receiver values (RV)
Each study will be conducted in 3 waves:
T0: Prescreening/baseline
To ensure balanced designs, participants will be prescreened using values measures (MO) and items to assess baseline levels of DVs 1-3.
T1: Intervention (Ca. 7 days post-screen)
Stimuli
A short story about a science-curious messenger who has undergone a transformation from being highly skeptical about the health risks of pandemics and vaccine safety to deeply concerned. Messenger values will be manipulated for each study as described above.
Measures: DVs 1-2, M1, M2
T2: Follow-up study (2-weeks post T1)
Approximately two weeks after T1, all participants will be invited to participate in a follow-up study measuring DVs 1-3.
Sample Size
To determine sufficient sample size for testing the hypotheses, we ran power analysis for the moderated serial mediation model outlined. This was done with different effect sizes to get a better understanding of the trade-off between detectable effects and sample size. Based on this analysis, N should be 400 as it would give a power of 0.9 with a standardized effect size of 0.2 for all direct effects (this means that the indirect effect that goes through two mediators would be 0.20.20.2 = 0.008). As two different models were going to be tested, the alpha-level was set at 2.5%. To account for outliers and exclusions, we increased the final sample by 5%, (to 420 per study).
Data analysis
Confirmatory. To analyse the hypotheses, we will run a moderated serial mediation model with two serial mediators (counterarguing, identification) with a moderated effect of receiver’s values (dummy coded) on the link between messenger’s values (dummy coded) and counterarguing. The Model is performed on all dependent variables.
Exploratory. To explore whether science curiosity can be nurtured, we will investigate whether the intervention influences post-narrative self-reported science curiosity at T2 to assess change from T0.
Study Costs
Open Science
All studies will obtain ethics approval and have been pre-registered at AsPredicted.org (#70667). All anonymized data, scripts, and protocols will be made available in Open Science Framework. Apart from dissemination to conferences (e.g., SJDM, IAMCR) and other key networking events (e.g., Nudgestock, health research fora), we intend to publish the results in an academic journal (e.g., Journal of Experimental Psychology: Applied or Public Understanding of Science) as an open access article.
References
- Kahan, D.M., ‘Ordinary science intelligence’: a science-comprehension measure for study of risk and science communication, with notes on evolution and climate change. Journal of Risk Research, 2017. 20(8): p. 995-1016.
- Kahan, D.M., et al., Science Curiosity and Political Information Processing. Political Psychology, 2017. 38(S1): p. 179-199.
- Morris, B.S., et al., Stories vs. Facts: Triggering Visceral Response to Climate Change Climatic Change, 2019.
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