Developing Digital and Media Literacies in Children and Adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it