Bibliographic record
Abstract
The contemporary young reader learns from a very early age to read and interpret through a broad range of media. Literacies Across Media explores how a group of boys and girls, aged from ten to fourteen, make sense of narratives in a variety of formats, including print, electronic book, video, DVD, computer game and CD-ROM. This book records these young people over a period of eighteen months as they read, view and play different texts, demonstrating variations and consistencies of interpretative behaviour across different media.Margaret Mackey analyses how the activities of reading, viewing and playing intertwine and affect each other's development. Her in-depth research shows young readers developing strategies for interpreting narratives through encounters with a diverse range of texts and media. The study breaks new ground in its illustration and exploration of the impact of cross-media fertilisation on how young readers come to an understanding of how to make sense of stories. Literacies Across Media offers both a vivid account of a group of young readers coming to terms with texts and a radical perspective on the growth of a generation of young readers. It is thought-provoking, fascinating and highly informative reading not only for theoreticians interested in the reading process, but also teachers, librarians, parents and anybody involved with young people and their texts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".