Digital Inclusion Across the Americas and Caribbean
Bibliographic record
Abstract
This research brings together digital inequality scholars from across the Americas and Caribbean to examine efforts to tackle digital inequality in Uruguay, Chile, Peru, Brazil, Mexico, Cuba, Jamaica, the United States, and Canada. As the case studies show, governmental policy has an important role to play in reducing digital disparities, particularly for potential users in rural or remote areas, as well as populations with great economic disparities. We find that public policy can effectively reduce access gaps when it combines the trifecta of network, device, and skill provision, especially through educational institutions. We also note, that urban populations have benefitted from digital inclusion strategies to a greater degree. This underscores that, no matter the national context, rural-urban digital inequality (and often associated economic inequality) is resistant to change. Even when access is provided, potential users may not find it affordable, lack skills, and/or see no benefit in adoption. We see the greatest potential for future digital inclusion in two related approaches: 1) initiatives that connect with hard-to-reach, remote, and rural communities outside urban cores and 2) initiatives that learn from communities about how best to provide digital resources while respecting their diversely situated contexts, while meeting social, economic and political needs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".