Dimensions and barriers for digital (in)equity and digital divide: a systematic integrative review
Bibliographic record
Abstract
Purpose This integrative review was conducted to provide an overview of existing research on digital (in)equity and the digital divide in developed countries. Design/methodology/approach We searched academic and grey literature to identify relevant papers. From 8464 academic articles and 183 grey literature, after two levels of screening, 31 articles and 54 documents were selected, respectively. A thematic analysis was conducted following the steps suggested by Braun and Clarke and results were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Findings The results showed that most articles and papers were either from Europe or North America. Studies used a range of research methods, including quantitative, qualitative and mixed methods. The results demonstrated four major dimensions of the digital divide among various vulnerable groups, including digital literacy, affordability, equity-deserving group-sensitive content and availability or access to infrastructure. Among vulnerable groups, low-income people were reported in the majority of the studies followed by older adults, racial and ethnic minorities, newcomers/new immigrants and refugees, Indigenous groups, people with disabilities and women. Most reported barriers included lack of access to the internet, digital skills, language barriers and internet costs. Originality/value To the best of our knowledge, there have been limited attempts to thoroughly review the literature to better understand the emerging dimensions of digital equity and the digital divide, identifying major vulnerable populations and their unique barriers and challenges. This review demonstrated that understanding intersectional characteristics (age, gender, disability, race, ethnicity, Indigenous identity and immigration status) and their interconnections is crucial for analyzing the dynamics of digital (in)equity and divide.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".