Exploring Digital Inclusion in Loíza, Puerto Rico: Evidence from the Project OCEBAL
Bibliographic record
Abstract
For over a decade, federal and state government, as well as nongovernmental organizations in the United States, have asserted the goal of digital equity as necessary for civic, economic, and political participation in society. This study adopts a quantitative approach to explore the project OVERCOME21: ConnectED2Health: Expanding Broadband Access to Loiza, Puerto Rico. This project was organized by US Ignite in partnership with Libraries Without Borders US. The analysis focuses on exploring three themes: 1) Access and adoption to internet services, 2) Availability of internet-enabled devices to support users’ online activities, and 3) Digital literacy. A pre-survey (ex-ante) and post-survey (post-ante) were administered before and after access to internet services and digital literacy and agile learning workshops. Households and individuals at selected communities were the analysis units for the pre-survey sample (n = 98), and post-survey sample (n = 80). Ex-ante findings show that most participants were single females who identified themselves as black or African American, and high school (46.4%) was the highest level of education. One-third of participants didn’t have internet service and reported an annual income of less than $15,000. Additionally, most participants (72.6%) didn’t know the term digital literacy, nor had they participated in a digital literacy workshop (82.5%). After digital literacy workshops ex-post data showed that more than half (71.2%) of participants knew the term digital literacy, and more than half (58.2%) had participated in a digital literacy workshop organized by the project. Ex-ante and post-ante data showed that most participants (95%) didn’t use broadband internet services for telehealth services, and over 50% of participants expressed concerns about the safety of personal information. Although data collection results are not representative of selected communities, research findings serve to contextualize digital equity and digital literacy initiatives as well as contributing to a research topic with social and public policy implications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".