Literature and Bullying: Teenage and Children Novels on School Bullying Prevention
Bibliographic record
Abstract
Juvenile delinquent or deviant behavior appears in various forms in modern academic reality. It is widely known under the international term "school bullying". The systemic view of an educational organization as it studies the variety of school and social system parameters that explain and contribute to the emergence of problematic behaviors in schools positively contributes to a better understanding of that behaviors considered within the framework of interactions that generate and reproduce it. Qualitative literary works, by which this phenomenon is approached in a novel way and the "omnipotent narrator" dominates, provide the possibility of a holistic and systemic view and indirect aids in strategies for preventing, detecting and curing the offending incidents in modern schools. The purpose of this paper is to connect the phenomenon of bullying appearing in teenage and children novels with the way that texts could illuminate and enlighten youth consciousness in order to become safe guides or useful paradigms in their everyday life. The texts examined under present study are the novels (a) Thirteen Reasons Why, by Jay Asher (b) Finding Audrey, by Sophie Kinsella and (c) Together, by Eleni Priοvolou.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".