Linguistic Analysis of Selected TV Cartoons and Its Impact on Language Learning
Bibliographic record
Abstract
The new generation is fascinated by the overwhelming exposition of media. Today, media is performing powerful role in the mental growth and emotional development of children. At the very first stage of cognitive development, children copy the words and expressions used in their surroundings. Cartoons and language used in them directly affect cognitive and linguistic development of children. The present study dissects the linguistic patterns and ideologies used in cartoons shown in Pakistani media. It further attempts to overlook the impact of linguistic features of cartoons on language learning propensities of children. The study adopted mixed method research design by following qual-quan approach. The linguistic analysis of the cartoons was done qualitatively whereas its impact on children’s language was measured through quantitative way. 100 students and 100 teachers were selected to determine the sample by applying random sampling technique. Self-developed questionnaire was used to collect data from the respondents. The collected data shows that cartoons are source of education, entertainment and information for children. Children can improve their language competencies by watching cartoon. However, students were found using many Hindi words in their daily conversation. Linguistic benefits of cartoon language collide with the cultural threats faced by a large number of parents. The findings of the study recommend that children should be shown level oriented and culturally specific cartoons so that students may get maximum linguistic benefits from them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".