Does Ageism Widen the Digital Divide? And Does It Vary by Gender?
Bibliographic record
Abstract
Abstract Existing literature informed that ageism might affect Internet use among older adults, further widening the digital divide among age groups. However, little empirical studies have been conducted on this topic. Our study aims to investigate (1) the current use of the Internet by gender, (2) the association between ageism and Internet use, and (3) potential gender differences. A cross-sectional data drawn from the 2016 Health and Retirement Study (HRS) was analyzed. Separate multiple regression analyses were conducted by gender to determine the varying impact of ageism on Internet use. We used two types of ageism (1) internal ageism (ingroup discrimination) and (2) external ageism (discrimination from external entities) to observe each type’s contribution to Internet use. About half of the sample (52% male and 54% female) reported using the Internet “daily,” while a quarter (26% male and 25% female) responded, “never/not relevant.” No significant differences between gender were found in levels of Internet use, the rates of external ageism, or the degree of internal ageism. A higher level of ageism was associated with a lower level of Internet use. Interaction effects between age groups and ageism varied across gender: external ageism had interaction effects on men’s Internet use whereas internal ageism showed significant results for women. Our findings suggest that ageism may influence Internet use and its impact differs by gender. Gender-tailored intervention strategies should be developed to help older individuals to diminish the adverse effects of ageism on Internet use.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".