Media literacy : transforming curriculum and teaching
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
The National Society for the Study of Education. Board of Directors of ther Society, 2005-2006 Contributors to the Yearbook. Reviewers for the Yearbook. Living Within the Media, Erin Griffin. Chapter. 1. Overview: What is Media Literacy, Who Cares, and Why? (Gretchen Schwarz). Part One. Understandng Media Literarcy. 2. Uninformed in the Information Age: Why Media Necessitates Critical Thinking Education. (J. Lynn McBrien). 3. Why Media Literacy Matters in American Schools. (Ladislaus Semali). 4. How the Media Teach. (Carlos Cortes). 5. Media Literacy and the K-12 Content Areas. (Renee Hobbs). 6. Critical Thinking for the Cyberage. (Julie D. Frechette). 7. The Shadow Curriculum. (Pamela U. Brown). Part Two. Doing Media Literarcy in the Schools. 8. The Canadian Experience: Leading the Way. (John J. Pungente, Barry Duncan, and Neil Andersen). 9. Teachers Need Media Literacy, Too! (Sandra K. Goetze, Diane S. Brown, and Gretchen Schwarz). 10. Media Literacy Education: Lessons from the Center for Media Literacy. (Elizabeth Thoman and Tessa Jolls). 11. The Practice and Principles of Teaching Critical Literacy at the Educational Video Center. (Steven Goodman). 12. Obstacles, Challenges, and Potential: Envisioning the Future. (Gretchen Schwarz). Commentaries. Researching Media Literacy: Pitfalls and Possibilities. (Roy F. Fox). Merging Media and Science: Learning to Weigh Sources, Not Just Evidence. (Marlene Their). Media Literacy: A Powerful Tool for Parents and Teachers. (Carla Crockett). The Library Media Center: At the Center of Media Literacy Education. (Angel Kymes). Media Literacy for a Future Teacher. (Cheri Maynard). Terrain in Transition: Reflections on the Pedagogy of Media Literacy Education. (Faith Rogow). Name Index. Subject Index.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 it