Digital literacies and children’s personalized books: Locating the ‘self’
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it