A Semiotic Study of Contemporary Middle Eastern Internal Dilemmas in Arab News Cartoons
Bibliographic record
Abstract
Many parts of the Middle Eastern region have a history of persistent and long-term crises. The media, and particularly the news media, endeavors to highlight these issues in various forms. One established format among them is caricatures, or cartoonish representations, which retain a visually captivating quality for the intended audience. Undeniably, cartoons depict the bitter realities in candid yet convincing forms. In this regard, the present study aims to analyze the Arab News cartoons that depict the internal predicaments faced by the selected Middle Eastern countries. The primary objective of this study is to examine the intricate relationship between semiotics and socio-political intricacies in the selected regions. This study employs Barthes’ semiotic lens theory to investigate the methods employed by the cartoonist in conveying messages, creating narratives, and interacting with the socio-political environment. The results reveal that the caricatured representations effectually depict several underlying causes and conflicts that fuel the internal chaotic situation inside the region, using signs, symbols, and pictorial rhetoric. These findings help in understanding the essence of the challenges faced by the chosen Middle Eastern nations quite meritoriously. At the same time, the results endorse cartoons as an authentic medium for discussing such harsh realities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".