Empowering Minds: Multimodal Literacies, Fanfiction, and Inclusive Education
Bibliographic record
Abstract
This article critically examines the intricate relationship between literacy and multimedia, focusing on multimodal literacies within the framework of educational equity. It navigates intersections among literacy, social-emotional learning, and educational equity, expanding from the analysis of fanfiction as mentor texts to the transformative empowerment of communities through technology integration. Connections between literacy engagement and cultural practices are explored through the lens of enhancing opportunities for English language learning. By tracing the historical roots of fanfiction and scrutinizing its contemporary manifestations in popular culture, this article explores the potential of multimodal literacies. These concepts cultivate literacy skills and foster creativity, empathy, and inclusivity—especially crucial for diverse learners, including those embarking on the journey of English language acquisition. Serving as a comprehensive resource, the article provides nuanced insights into the multifaceted ways in which multimodal literacies can effectively bridge the engagement gap. It offers practical applications, outlining a path towards a more equitable and enriching educational landscape in the digital age, with a specific focus on promoting linguistic proficiency among English language learners. The article stands as a valuable guide for educators, researchers, and practitioners, offering concrete strategies for leveraging multimodal literacies to create inclusive and empowering learning environments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".