Integrating Media and Popular-Culture Literacy With Content Reading
Bibliographic record
Abstract
Teaching media literacy and using popular culture texts (e.g., television programs, magazines, videos, Wlms, newspaper advertisements) to enhance students’ language and literacy development has proven successful in Australian and Canadian classrooms (Desmond, 1997; Quin & McMahon, 1992). In the United States, there is a slowly growing movement toward implementing similar instruction. Advocates of employing media literacy and popular-culture texts to enhance students’ understandings in U.S. schools are highly diverse. They include educators across all subjects and levels: community, health, political, and parent organizations; psychologists, counselors, and social workers; cultural critics and media producers. Their aims are also mixed. Some assume “protectionist” or “inoculationist” stances that seek to protect children from objectionable content and values in mass media (e.g., in television, Wlms, or music videos), or to “inoculate” them through education against the dangers of popular culture through education. Others employ media literacy education to further goals (presented earlier in this book) of connecting literacy proWciencies, and using varied print, electronic, and experiential sources; stress critical thinking and prepare democratic citizens (Considine, 1987; Considine, Haley, & Lacy, 1994); and promote self-empowerment or social action through analysis and production of multiple texts (Semali & Watts Pailliotet, 1999).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".