Old and afraid of new communication technologies? Reconceptualising and contesting the ‘age-based digital divide’
Bibliographic record
Abstract
Despite sociological attempts to critically address an age-based digital divide, older adults (65+) continue to be portrayed in the academic literature and public discourse as a homogeneous group characterised by technophobia, digital illiteracy, and technology non-use. Additionally, the role of socioeconomic factors and personal contexts in later life are often overlooked in studies on technology adoption and use. For example, older adults who are identified as least likely to use technology (frail, care-dependent, low socioeconomic/educational backgrounds) are typically described as a uniform cluster. Yet, research on digital technology use with this group remains scant – so what can we learn from studying technology adoption among them? This article discusses long-term deployment of new communication technologies with such a group of older adults, shedding light on the dynamics of technology adoption and contexts of use/non-use. It is based on a case study approach and a cross-cultural perspective, using Canadian and Australian mixed-methods research from two projects that included interviews, psychometric scales, and field observations. We present cases from these projects and contest the simplistic notion of an age-based digital divide, by drawing on Strong Structuration Theory to explore the interconnection of agency, structure, and context in the sociotechnical process of technology adoption and use/non-use among older adults.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".