Exploring Topics and Genres in Storytime Books: A Text Mining Approach
Bibliographic record
Abstract
Objective – While storytime programs for preschool children are offered in nearly all public libraries in the United States, little is known about the books librarians use in these programs. This study employed text analysis to explore topics and genres of books recommended for public library storytime programs. Methods – In the study, the researchers randomly selected 429 children books recommended for preschool storytime programs. Two corpuses of text were extracted from the titles, abstracts, and subject terms from bibliographic data. Multiple text mining methods were employed to investigate the content of the selected books, including term frequency, bi-gram analysis, topic modeling, and sentiment analysis. Results – The findings revealed popular topics in storytime books, including animals/creatures, color, alphabet, nature, movements, families, friends, and others. The analysis of bibliographic data described various genres and formats of storytime books, such as juvenile fiction, rhymes, board books, pictorial work, poetry, folklore, and nonfiction. Sentiment analysis results reveal that storytime books included a variety of words representing various dimensions of sentiment. Conclusion – The findings suggested that books recommended for storytime programs are centered around topics of interest to children that also support school readiness. In addition to selecting fictionalized stories that will support children in developing the academic concepts and socio-emotional skills necessary for later success, librarians should also be mindful of integrating informational texts into storytime programs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".