Too Old For Technology? How The Elderly Of Lisbon Use And Perceive ICT
Bibliographic record
Abstract
The elderly have traditionally been an excluded group in the deployment of Information and Communication Technologies (ICT). Even though their use of ICT is increasing, there is still a significant age-based digital divide. To empower elderly people’s usage of ICT we need to look at their patterns of usage and perceptions. To understand how Portugal’s elderly (65 and above) use and perceive mobile phones, computers and the Internet, we surveyed a random stratified sample of 500 individuals over 64 years of age, living in Lisbon. Of those surveyed, 72% owned a mobile phone, 13% used computers, and 10% used the Internet. The quantitative data was followed-up by ten qualitative (semi-structured) interviews. The implications of the results are discussed herein. Keywords: Elderly, Aging, ICT, Ageism, Digital Divide, Mobile phones, Computers, Internet, Portugal, “faux users”
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".