Bibliographic record
Abstract
An approach to literacy that understands it as lived and experienced in the everyday across varied spaces and populations.This book approaches literacy as lived and experienced in the everyday. A living literacies approach draws not only on such official, schooled activities as reading, writing, speaking, and listening but also on such routine, tacit activities as scrolling through Instagram, watching news footage, and listening to music. It goes beyond well-worn framings of literacy as an object of study to reimagine literacy as constantly in motion, vital, and dynamic, filled with affective intensities.A lived literacies approach implies a turn to activism, to hopeful practice, and to creativity. The authors examine literacies through a series of active verbs: seeing, disrupting, hoping, knowing, creating, and making. Case studies—ranging from an exploration of photography as a way to shift perspectives to a project in which adults teach young people how to fish—show lived literacies in both theory and practice. With these chapters, the authors position literacy differently. They make it possible to see literacy in everyday activities, woven into the modes of seeing and knowing. By disruption and activism, literacy can encompass a wide array of practices—exchanging information at a school gate or making a collage. Grounding theory in the sites and spaces of their research, working with artists, photographers, poets, and makers, the authors issue a call to action for literacy education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".