Digital inequalities among internet users before and during the COVID-19 pandemic: A comparison from two cross-sectional surveys in Slovenia
Bibliographic record
Abstract
The further digitization of society during the COVID-19 pandemic had implications for differential internet access and related social and economic outcomes. The pandemic impacted all three levels of the digital divides, as it affected how people accessed the internet, their internet skills and usage patterns. The literature, however, provides only a limited understanding about how these changes are interrelated at different levels of the digital divides. Building on the model of compound and sequential digital exclusion, this study examines the sequential pathways between the three digital divide levels before and during the pandemic, using two datasets obtained through cross-sectional surveys conducted in 2018 and 2022 from representative samples of Slovenian internet users ( N 2018 = 814, N 2022 = 802). Path and multi-group analysis results confirmed strong sequential pathways between the three digital divide levels before and during the pandemic, suggesting that despite a significant increase in internet access and breadth of uses, digital inequalities among users continue to persist following the pandemic. Moreover, the interaction effects of age and education on the relationships between the three digital divide levels were found to be almost unchanged, indicating that the pandemic did not affect the role of social inequalities in shaping digital inequalities. • Three levels of digital inequalities before and during the pandemic are explored. • Analysis of two representative cross-sectional survey datasets from 2018 and 2022. • Significant increase in internet access and uses did not lessen digital inequalities. • Almost invariant interaction effect of age and education with digital inequalities. • The pandemic did not reduce digital inequalities spurred by social inequalities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".