Complicating the Resilience Model: A Four-Country Study About Misinformation
Bibliographic record
Abstract
The resilience model to disinformation (Humprecht et al., 2020, 2021) suggests that countries will differ in exposure and reactions to disinformation due to their distinct media, economic, and political environments. In this model, higher media trust and the use of public service broadcasters are expected to build resilience to disinformation, while social media use and political polarization undermine resilience. To further test and develop the resilience model, we draw on a four-country (the US, Canada, the UK, and France) survey conducted in February 2021. We focus on three individual-level indicators of a lack of resilience: awareness of, exposure to, and sharing of misinformation. We find that social media use is associated with higher levels of all three measures, which is consistent with the resilience model. Social media use decreases resilience to misinformation. Contrary to the expectations of the resilience model, trust in national news media does not build resilience. Finally, we consider the use of public broadcasting media (BBC, France Télévisions, and CBC). The use of these sources does not build resilience in the short term. Moving forward, we suggest that awareness of, exposure to, and reactions to misinformation are best understood in terms of social media use and left–right ideology. Furthermore, instead of focusing on the US as the exceptional case of low resilience, we should consider the UK as the exceptional case of high resilience to misinformation. Finally, we identify potential avenues to further develop frameworks to understand and measure resilience to misinformation.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".