Linguistic Analysis of Preschool Literature Related to Peer Victimization
Bibliographic record
Abstract
Abstract Peer victimization occurs in various social contexts, including among preschool children. As language socialization often occurs through children’s literature, interventions have frequently utilized picture books with young children. As words convey psychological meaning, the current study utilized linguistic analysis to determine how the words of bully characters and victim characters differed. The Linguistic Inquiry and Word Count program was used to evaluate the bully and victim transcripts from 69 books written in English on differential use of emotional language, cognitive language, temporal focus of language, overall style of language, and first-person-centered pronoun usage. Books selected included popular children’s books written in English, appropriate for age groups 3 − 5, and published in the countries of the United States, Canada, and England. Popular children’s books were identified using a list created by Oppliger and Davis ( Early Childhood Education Journal, 44 (5), 515–526, 2016) that delineated books following a traditional storyline circulated to groups aged 3 − 5 with high internet sale rankings. The language used by bully and victim characters differed in several significant ways related to specific emotion, insight, temporal focus, authenticity, analytical thinking, gendered language, and first person-centered pronoun usage suggesting the social impact of storytelling on children’s understanding of bullies and victims. Implications surrounding the role of language socialization and the use of children’s literature for bully prevention programs are discussed.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".