The digital divide and population aging in Chile: diagnosis, public policies, and intergenerational impact
Bibliographic record
Abstract
Chile is undergoing a rapid demographic transition, with projections indicating that by 2050, 24% of its population will be over the age of 60 in the context of low fertility (1.4 children per woman). Within that context, this article examines the digital divide from a multidimensional perspective, integrating demographic, economic, and technological data, focusing on intergenerational inequalities, particularly among older adults in the Valparaíso Region, presenting a preliminary diagnosis. Through a cross-referenced and comparative analysis of national surveys (CASEN, 2022; Criteria, 2024), national reports, and international standards (UNESCO, OECD), a systematic study of public policies and initiatives based on successful local, national, and international models (such as Estonia and Canada) is proposed. These initiatives aim to integrate infrastructure, digital literacy, and technological security to mitigate socioeconomic risks and promote intergenerational inclusion through long-term planning
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".