Who is concerned about digitalization? The role of digital literacy and exposure across 30 countries
Bibliographic record
Abstract
Rapid digitalization has unleashed widespread digital concerns, namely concerns about the potential harms associated with digital technology use, such as privacy loss, blurred work-family boundaries, and misinformation. Analyzing nationally representative data from the European Social Survey (2020–2022; N = 49,665), we present the first evidence across 30 countries on the prevalence of and sociodemographic variations in digital concerns, as well as how digital literacy and exposure relate to these concerns. Our findings reveal high levels of digital concerns, averaging 0.65 across countries on a 0–1 scale, ranging from 0.47 in Bulgaria to 0.74 in the Netherlands. Following a concave age pattern, adults aged 25–44 years report greater concerns compared to younger people and older adults. More educated individuals report greater digital concerns than those with less education. Digital concerns, however, vary little across the income spectrum or from big cities to remote villages. Exhibiting a positive digital literacy–concern link, those with greater digital literacy are more concerned about digital technologies’ potential harms. This digital literacy–concern link intensifies with digital exposure, which is measured through both individual-level technology use and country-level internet coverage. Our study highlights digital concerns as an understudied yet prominent feature of everyday life in today’s societies. Global agendas for improving digital literacy and engagement should incorporate efforts to address not just digital technologies’ ramifications but also people’s concerns about them.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".