The Competition Between Al-Jazeera’s Arab News Diversity and US Channels: Content Analysis of Iraq War
Bibliographic record
Abstract
This paper examines Al-Jazeera and CNN channels, as a source of news diversity. Also, to gain a broad and in-depth understanding of how culture affects news reporting, with a particular focus on the Arab culture as compared to American culture using Al-Jazeera as the Arab source of news and CNN as a western news source. One of the key issues will considered is the role of news, diversity of news, organization in society and in international relations. Another key issue is an examination of the variety of CNN and Al-Jazeera’s Arab culture news organizations, its methods, reporting style, technologies used, etc. the content analysis of 7 transcripts, related to the beginning of Iraq War, from 2003 to 2005, during President Bush presidency, from both channels used to diminish the differences between them. The research questions are: in what ways does Al-Jazeera compare to a Western news organization like the CNN? In what ways do specific news reports from Al-Jazeera compare to American news reports of the same event? The Literature Review includes different perceptions, concepts, content analysis and transcripts from both channels, Al-Jazeera and CNN. The topic of this research is the competition between Al-Jazeera’s Arab news diversity and US channels in the USA. The research, then, is to examine how news influences the way that people make meaning regarding events. The study focus will be on the Arabian Gulf media, power of Al-Jazeera and the comparison between the Western and eastern media specially Iraq war news. The author fined that Al-Jazeera has faster grown market in the west than CNN. In spite of its bias news it broadcasts different news than CNN and western channels. Furthermore, there is a need of further studies about the comparisons between Western and Eastern media. Keywords: Al-Jazeera and CNN; News reporting; Al-Jazeera global power; Social media theory.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".