Ageism and Technology: The Role of Internalized Stereotypes
Bibliographic record
Abstract
Ageist views have typically held that older persons are poor, frail, and resistant to change. One facet of this portrait of the older population has to do with their lower willingness and capability to learn and with their decreased openness to change (Cutler). Many of these ageist views are held by young people, resulting in a bias about the development and designs of different technologies. However, these same views are sometimes shared by older people themselves, resulting in a reluctance to adopt different technologies and the underestimation of their own performance or technology skills (Beckers et al.). In the current work, we analyze the reciprocal relationship between ageist stereotypes and technology, focusing on the implications of negative stereotypes of older people. We emphasize the self-fulfilling prophecy that technology, designed mostly by young people with the youth market in mind, creates prototypes that are more difficult for older people to use and algorithms that often fail to predict the habits, interests, and values of older people (Rosales and Fernández-Ardèvol). We also examine the role of stereotype threat impacting older people’s performance and technology adoption; for example, situation-specific anxiety when older people face younger adults who present greater digital skills (Ivan and Schiau).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".