Exploring Privilege in the Digital Divide: Implications for Theory, Policy, and Practice
Bibliographic record
Abstract
Background and Objectives: The digital revolution has resulted in innovative solutions and technologies that can support the well-being, independence, and health of seniors. Yet, the notion of the "digital divide" presents significant inequities in terms of who accesses and benefits from the digital landscape. To better understand the social and structural inequities of the digital divide, a realist synthesis was conducted to inform theoretical understandings of information and communication technologies (ICTs); to understand the practicalities of access and use inequities; to uncover practices that facilitate digital literacy and participation; and to recommend policies to mitigate the digital divide. Research Design and Methods: A systematic search yielded 55 articles published between 2006 and 2016. Synthesis of existing knowledge, combined with user-experience elicited through a deliberative dialogue session with community stakeholders (n = 35), made visible a pattern of privilege that determined individual agency in ICT access and use. Results: Though age is consistently centralized as the key determinant of the digital divide, our analyses, which encompassed both van Dijk's resources and appropriation theory and intersectionality, appraised this notion and revealed that age is not the sole determinant. Findings highlight the role of other factors that contribute to digital inequity among community-dwelling middle-aged (45-64) and older (65+) adults, including education, income, gender, and generational status. Discussion and Implications: Informed by results of a realist synthesis that was guided by intersectional perspectives, a conceptual framework was developed outlining implications for theory, policy, and practice to address the wicked problem that is the digital 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 | 0.080 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.012 | 0.071 |
| Scholarly communication | 0.030 | 0.048 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".