A Critical Discourse Analysis of Newspaper Articles from Bangladesh and Canada on Palestine-Israel Conflict
Bibliographic record
Abstract
This thesis presents a Critical Discourse Analysis (CDA) of how Bangladeshi and Canadian newspapers represent the ongoing conflict between Palestine and Israel in editorials/opinions/interviews and reprinted news articles from October 2023 to January 2025. The motivation behind this research was to examine how newspapers from Bangladesh and Canada reflect ideological perspectives on this conflict, as the newspapers from these two countries have received limited attention in CDA studies on the Palestine-Israel conflict. The study analyzed a total of 120 news articles, sourced from two prominent newspapers in each country: The Daily Star and The Prothom Alo English (Bangladesh), as well as The Globe and Mail and The National Post (Canada), utilizing Van Dijk's Socio-Cognitive Model. The study investigated the texts for their thematic structures, five macro-devices, and 12 micro-devices of Van Dijk's model. The analysis shows that both countries' newspapers report common casualties and suffering on both sides. However, Bangladeshi newspapers highlight humanitarian concerns and the victimization of Palestinians, often portraying Israel as a brutal force. In contrast, Canadian newspapers emphasize Israeli victimhood and suffering, and portray Hamas as a menace. The analysis demonstrates the media’s attempts to influence people's perception of events through linguistic means. It provides insights into the different stances employed in the newspapers of the two countries on portraying one of the cruelest and enduring geopolitical conflicts of this generation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 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".