Bibliographic record
Abstract
Do older people participate in this major conveyor of twenty-first century images? Many of the images in media and marketing today are conveyed by the Internet. To what extent do older people participate in this vehicle that has such influence on attitudes and perceptions in current culture? Older people can and do go online to get London theater reviews, track the elephant migration in Africa, reserve a hotel room in New York, participate in a book club, receive the latest picture of a grandchild, join a that room, or play bridge. The information highway is wide and long, and older people are aboard. The computers may be hand-me-downs from their children or grandchildren, used in a library, or their own purchases. A 1999 independent research study funded by Microsoft in conjunction with the American Society on Aging shows that 30 percent of older people in the United States between the ages of 50 and 79 own and use a computer. This study also shows that computer buyers over the age of 55 represented more than 23 percent of all personal computer purchases in a quarter of 1998, showing a growth in the number of purchases of over 150 percent from 1997 (Microsoft Corporation, 1999). According to the U.S. Commerce Department, 29.6 million Americans over age 50 used the Internet in 2000, compared with 19.3 million users in 1998. In increasing numbers, older people have access to computers, have learned how to use them, and can navigate the Internet. Like sophisticated users of all ages they can make purchases, do their banking, and navigate the Internet media with its new and specialized information searching systems. Older would-be computer users had to start at the beginning when they were persuaded by family and friends, had their own fears of being left behind, or were challenged by the potential of a new technology. In addition to being self-taught or helped by a friend or relative, older people can learn how to use a computer through classes offered in continuing education classes in high schools, community colleges, and universities, through recreation centers, and special programs designed for older adults. A successful example of these special programs is SeniorNet, a not-for-profit organization in California whose aim is to provide older adults with computer education and access to computers and the Internet in order to enrich their lives. Now fifteen years old, SeniorNet (www.seniornet. org/research) has over 200 learning centers and 39,000 members who have participated in their computer classes. Classes in these learning centers are led by volunteer instructors and coaches who share their expertise in and enthusiasm for computers. In addition, AARP provides online basic how-to programs. Health organizations offer training programs on how to gain access to health information via computer. Government grants provide training for older people to learn how to use computers to enter or reenter the labor market. Many opportunities exist for older people to learn in the marketplace, educational organizations, as well as in programs specifically designed for elders. Learning how to use the computer with its software applications is only the initial step. Accessing the Internet is more complicated and is becoming ever more complex. Keeping up with all the new and specialized informationsearch systems is a discipline in itself. As early as 1997, the Department of Health and Human Services announced the Computers for Seniors program designed to help give older Americans access to the Internet and help them make better use of Medicare, Medicaid, and other HHs programs. AARP provides a guide listing some of the Internet search tools that they have found to be useful for older people (www.aarp.org). Included is Internet Development for the Aging Network: Online Resources, developed by Saadia Greenberg of the U.S. Administration on Aging for distribution by the National Aging Information Center. Major companies like Microsoft and Intel support research on the older adult market, donate hardware and software to senior programs, and continue to woo older potential buyers. …
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".