Coding Futures: Rethinking Digital Literacy Policies in Senior High Schools as Catalysts for Inclusive Economic Growth
Bibliographic record
Abstract
Digital literacy has become a key foundation for educational innovation and economic participation in the 21st century. As global economies transition toward digitalization, integrating digital competencies into senior high school curricula is increasingly viewed as essential for supporting Sustainable Development Goal 8 (SDG 8), which emphasizes decent work and inclusive economic growth. This study investigates the role of public policy in shaping digital literacy education in senior high schools through a comparative case study of Indonesia, the Philippines, and Canada. Employing qualitative policy analysis, the research explores how each country’s policy framework conceptualizes, implements, and evaluates digital literacy initiatives. The analysis focuses on curriculum integration, resource allocation, and stakeholder involvement, while also examining how these programs contribute to employability and economic resilience. The findings reveal notable differences in policy design and institutional commitment, with Canada demonstrating a more systematic integration of digital literacy, the Philippines emphasizing access and equity, and Indonesia facing challenges related to resource disparities and curriculum consistency. These variations illustrate how national contexts influence the inclusiveness and effectiveness of digital literacy policies. The study concludes with recommendations for policymakers to design context-sensitive, equitable, and future-oriented digital literacy strategies that align with labor market demands and promote sustainable economic growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".