The Portrayal of Discourse of Violence in Fantasy Fiction and Its Impact among Adolescents and Early Teenagers
Bibliographic record
Abstract
Fantasy fiction is an extremely popular genre that acts as easy and interesting reading material, also mostly acquired by parents or peers as a gift. Its availability may also be promoted by local bookstores or the School library. It has been shown by multiple studies that media that display violence adversely affects the mental health of children. Unlike movies and films, there is no censorship associated with literature. This article is designed to elaborate upon these effects. The media referred to in this article is print media. A large amount of research is focused largely on visual media like television, digital games as well as films. However, the way by which violent acts are projected on the psychology of children through print media still remains to be comprehensively researched. The genre of print media referred to in this article is popular fantasy fiction. This particular genre of fiction is vastly popular and conveniently available to children. The study includes primarily adolescents and early teenagers in the age group 10 years to 16 years. The consumption of media violence through fantasy fiction during the early and impressionable years has been known to predict aggression, aggressive behaviour and stress among children, especially when the children under study are school-going. It also may most likely result in facing peer rejection and socially unwanted consequences. The context of the “General Aggression Model” or GAM is a complex and multifactorial concept that forms the basis of the findings. The purpose of the study is to validate and create a clear awareness about the effect of short-term as well as the long- term exposure of violence, on young children who are of an impressionable mindset. The study also aims to propose some measures of intervention that may be undertaken to reduce the effects of this exposure that lead to aggression and stress, which could be of a long lasting nature. The finding and outcomes of the study undertaken through the data analysis clearly supports its objectives.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".