Bibliographic record
Abstract
Editorial Older adults in a Modern WorldBy L.F. Carver, Editor-in-Chief Stereotypes that suggest older adults are resistant to technology may have had some accuracy in the 1980s but are now outdated and inaccurate. Technology has been evolving quickly over the past century, and older adults have been adapting to these changes just as quickly. Telephones went from a single wall mounted phone in the home to cordless handsets and cellphones, changes driven by the buying habits of people who are now older adults. The consumers who enthusiastically embraced home computers, then laptops and finally tablets are now middle aged or older adults.(Geron)technologies are those technologies that support aging adults – but these do not need to be limited to health-related devices. Older adults generally report feeling much younger inside than their chronological age and buy technology that supports them to engage in hobbies and activities that they did in their twenties and thirties. These technologies often mirror those chosen by their younger counterparts.Governments are recognizing the importance of AgeTech with investments in the development of these industries. For example, in late 2022/early 2023 the Canadian government announced a $47 million dollar investment (via the Strategic Innovation Fund) and the American National Institute on Aging (a2 Pilot Awards) unveiled $40 million for AgeTech over five years. Given the inaccuracies of traditional ageist stereotypes it is important to avoid surveillance and communal residential settings (e.g., long term care homes) as the focus for AgeTech, especially given most older adults are uninterested in living these facilities and technology that violates their privacy.Modern older adults are interested in technologies that enable them maintain independence, aging gracefully in place, in their own homes. However, they tend to reject technologies that impinge on privacy – especially those that involve 24/7 surveillance. Ambient assisted living, which generally involves cameras and pressure plates that record, and report, movement and activity are a favourite among caregivers and concerned family but are generally repudiated by older adults.Finally, older adults who refuse certain technologies are not ‘resistant’ to tech, they just don’t want technology to try to fool them or replace reality. If they enjoy travel, they want technology that allows them to continue to travel as long as possible, not virtual reality goggles. Older animal lovers don’t want a robot pet, they want technology to support them to continue to care for their non-human companions as long as possible.The most important thing for policy makers, technology companies and younger adults to remember is that older adults share many of the same consumer interests when it comes to technology as younger adults. In fact, instead of considering older adults as something different or ‘other’ than their younger counterparts, we need to remember that they are the same people as they were when they were younger, they just have more mileage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".