Cartoons as Educational Tools and the Presentation of Cultural Differences Via Cartoons
Bibliographic record
Abstract
The childhood of a person is shaped as per the conditions of his/her community. However, the childhood in our technology-based era is highly overwhelmed by the ubiquitous communication devices. As a pioneering type, television achieves in grabbing children's attention by using its multi-coloured and animated world. What is more, cartoons provide the children a great load of new ideas, allowing them to enrich their dream world as well as to improve their vocabulary and learn new games. These developments are then turned to permanent behaviours. This being the case, it becomes inevitable that these habits reflect the cultural and moral values of the countries depicted in cartoons. This, in turn, makes the children absorb the linguistic and behavioural traditions of those cultures. The present study delves into two well-known cartoons, one being Turkish, called “Pepee” and the other Canadian, called “Caillou”, with a view to investigating the ways they present their cultural values. The ways of presentation were assessed using content analysis, and also the differentiating cultural elements were identified.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".