Bibliographic record
Abstract
Narrative is central to human experience, and a key way that experience is made meaningful. Education and entertainment have both played a significant part in the evolution of children's narrative. In its liminal state during the 1500s, children's print narrative was primarily educational. Locke's theories of education in the 1700s encouraged "children playing and doing as children," and narrative slowly moved towards being entertaining as well as educational. Not until the 1800s, with the stories of Lewis Carroll, was narrative created solely for the entertainment of children. Throughout its development it has provided a way for shaping children's experience, reflecting how they fit into their society, and helping them construct meaning for themselves. As narrative evolved to find its rightful place in the mix of technology, education, and entertainment within children's print culture, so it is evolving within the rapidly developing digital environment. Authors, publishers, and producers are responsible for understanding how children respond to a digital environment, and for making the digital narrative a positive experience. This paper presents the history of children's literature as it has developed from oral tradition through print and now into digital environments. It draws parallels, particularly between education and entertainment, in children's print narratives and similar (but more rapid) developments in the evolution of digital narratives. In doing so, it aims to encourage a more positive attitude to the significant opportunities new technologies offer for reshaping the way in which narrative for children is conceived and presented so that it continues in its time honored role of constructing meaning in their lives.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".