Bibliographic record
Abstract
This chapter proposes the concept of Arts-Based Multiliteracies (ABM) grounded in arts literacies (Barton, 2020), critical literacies (Kaya et al., 2022), complexity thinking (Davis & Sumara, 2006), and expanded literacies, including multiliteracies (New London Group, 1996) and multimodality (Kress, 2009). The authors argue for the importance and potentiality of ABM and provide contextual examples to illustrate ways of making and communicating meaning through multimodal ensembles with the arts and other literacies. Arts literacies are threaded with expanded literacies, critical literacies, and complexity thinking to conceptualize Arts-Based Multiliteracies. Dimensions of Arts-Based Multiliteracies are described and potentials for ABM design are discussed. The chapter concludes with the challenges and promises of Art-Based Multiliteracies and with the hope that the embodied, emotional, and relational potentials of Arts-Based Multiliteracies may provoke social change and inspire and transform teaching and learning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".