Addressing digital equity and the digital divide
Bibliographic record
Abstract
This report summarizes existing research on digital (in)equity and the digital divide in developed countries, aiming to map emerging dimensions, identify vulnerable populations, and highlight barriers to digital inclusion. A comprehensive search of academic and grey literature yielded 31 academic articles and 54 grey documents after screening 8,464 academic and 183 grey sources. Thematic analysis results revealed four key dimensions of the digital divide which emerged: disparities in digital literacy, affordability, access to infrastructure, and equity-deserving group-sensitive content. Vulnerable populations most frequently studied included low-income individuals, followed by older adults, racial/ethnic minorities, immigrants/refugees, Indigenous communities, people with disabilities, and women. Common barriers included limited internet access, insufficient digital skills, language challenges, and high connectivity costs. This review underscores the scarcity of holistic analyses addressing the evolving dynamics of digital inequity and emphasizes the critical role of intersectionality, examining how age, gender, disability, race, ethnicity, Indigenous identity, and immigration status interconnect, in shaping digital exclusion. By consolidating evidence across disciplines, it advances understanding of systemic inequities and calls for targeted, inclusive policies to address the multifaceted barriers faced by marginalized groups in achieving digital equity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".