Putting the Sorting Hat on J.K. Rowling’s Reader: A digital inquiry into the age of the implied readership of the Harry Potter series
Bibliographic record
Abstract
Compared to the large body of research into gender, race and class in children’s literature, there has been little awareness of the social construction of age in this discourse. Analysing age in contemporary fiction for young readers gives insight in how present-day society models (people of) different ages, and given the decisive role that books play in shaping children’s worldviews, such research contributes to our understanding of how age norms are passed on across generations. This article explores the construction of age in J.K. Rowling’s Harry Potter in relation to the age of the implied reader. This case study provides a unique opportunity to study age, because the main characters in every volume ‘grow up’ together with the implied readers. This article traces the correlation between the evolutions in form and content in J.K. Rowling’s Harry Potter series on the one hand and an evolution in the age of its implied readership on the other. After scrutinising existing guidelines pertaining to the ideal age at which to read each book, we conduct our own digital analyses on the style and topics of the texts. As well as providing insight into the evolution of these features in the Harry Potter books, this article contributes to the ongoing discussions on the reliability of readability measures and the desirability of explicit age markers on books for young readers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".