ACHIEVING DIGITAL EQUITY FOR OLDER PERSONS WITH EMERGING TECHNOLOGY: THE CASE OF NORTH AMERICA
Bibliographic record
Abstract
Abstract The United Nations’ theme for the International Day of Older Persons 2021 was “Digital Equity for All Ages”. I define digital equity, then compare progress in North America, focusing on Canada, the USA, and Mexico for internet, smartphone, and broadband access. An age-related digital divide for internet and computer use, first identified in the USA in 1999, persists. Where year-to-year comparisons are available, I also discuss the impact of the pandemic on technology adoption. National data sets measuring internet use show that there are similarities and differences in the USA and Canada in factors influencing adoption. For smartphone ownership, Mexico showed gender differences favoring men, unlike the case in the USA and Canada. For broadband access in the USA inequity was seen as a function of age, race/ethnicity, education and income, and urban/rural residence. I discuss potential reasons for inequitable access and potential approaches to achieve greater equity.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".