Digital literacies and children’s personalized books: Locating the ‘self’
Bibliographic record
Abstract
This conceptual article discusses the role of digital literacies in personalized books, in relation to children’s developing sense of self, and in terms of assessing the potential impact of artificial intelligence (AI). Personalized books contain children’s data, such as their name, gender or image, and they can be created by readers or automatically by the publisher. Some personalized books are e-books enhanced with artificial intelligence, and some can be ordered as paperbacks. We discuss this use of children’s personal data in terms of the social location of the self with regard to subjective and objective dimensions. We draw on a map metaphor, in which objective space requires readers to locate themselves in an unknown ‘A-to-B’ space and subjective space provides an individually oriented world of ‘me-to-B’. By drawing on examples of personalized books and their use by parents and young children, we discuss how personalization troubles the borders between readers’ me-to-B and A-to-B space experiences, leading to possible confusion in the sense of self. We conclude by noting that AI-enhanced personalized texts can reduce personal agency with respect to formulating a sense of identity as a child.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".