Developing Digital and Media Literacies in Children and Adolescents
Bibliographic record
Abstract
In today's global culture and economy, in which individuals have access to information at their fingertips at all times, digital and media literacy are essential to participate in society. But what specific competencies must young citizens acquire? How do these competencies influence pedagogy? How are student knowledge, attitudes, and behaviors changed? What are the best ways to assess students' digital and media literacy? These questions underscore what parents, educators, health professionals, and community leaders need to know to ensure that youth become digitally and media literate. Experimental and pilot programs in the digital and media literacy fields are yielding insights, but gaps in understanding and lack of support for research and development continue to impede growth in these areas. Learning environments no longer depend on seat time in factory-like school settings. Learning happens anywhere, anytime, and productivity in the workplace depends on digital and media literacy. To create the human capital necessary for success and sustainability in a technology-driven world, we must invest in the literacy practices of our youth. In this article, we make recommendations for research and policy priorities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".