Bibliographic record
Abstract
New to picture-book research, this article considers the phenomenon of YouTube “read-aloud” channels. Unlike other “BookTube” channels which focus on reviews and recommendations, these channels consist of adults reading other people’s copyrighted stories in their entirety, sometimes with the addition of graphics or sound effects, but often with no transformation of the original work beyond the reader’s dramatized voice. Nevertheless, read-aloud channels can garner millions of views, and have turned YouTube into perhaps the largest online repository through which picture-books can be accessed in their entirety without paywalls.This article therefore considers how YouTube read-alouds remediate picture-book reading. I offer a case study of two popular read-aloud channels, @AwniesHouse and @KidTimeStoryTime. My analysis considers branding, audience, recommendations, and how the channels’ video structures mediate interaction with the material texts they feature. Locating YouTube read-alouds within the existing research, I demonstrate how these videos diverge from other forms of book adaptation, and from other modes of oral picture-book reading. I therefore conclude that YouTube read-alouds are a distinct form of picture-book engagement, with unique affordances and limitations which particularly affect the stasis, tactility, and interactivity of picture-book reading.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".