The Importance of Storytelling for Children and Adults Alike: Lewis Carroll’s Alice’s Adventures in Wonderland and J. M. Barrie’s Peter Pan
Bibliographic record
Abstract
Since the publication of Alice’s Adventures in Wonderland (1865) and Peter Pan (1904), much has been studied and said about these two stories and their authors Lewis Carroll and J. M. Barrie, respectively. They have undoubtedly become classics of children’s literature, and have been retold, reinterpreted, and performed over the years. Through their fantasy, both Alice and Peter deal with themes highly relevant to any child’s – and adult’s – development. However, during my study of the two tales, I found a lack of interest and research towards the actual concept of storytelling. Both Carroll and Barrie originally created the stories to tell them to real children; and within each tale, the characters constantly tell stories to one another. Societies and communities are shaped by their stories, and telling them to children can serve many purposes, such as teaching a valuable lesson, learning how to cope with one’s own struggles, creating a strong bond between narrator and child, or simply – but not less importantly – taking pleasure in them. The aim of this dissertation is to explore the process of storytelling in Alice’s Adventures in Wonderland and Peter Pan, as well as its roles, in order to explain its significance, as an essential part of growing up and becoming oneself.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".