Bibliographic record
Abstract
This essay critically examines CNN’s portrayal of Palestinians during the Israeli-Palestinian conflict, focusing on how Orientalist and racialized narratives shape public perceptions of Palestinian identity. The analysis argues that CNN’s coverage perpetuates stereotypes by framing Palestinians as either passive victims or violent aggressors, depending on the context. This dual framing obscures the political and historical complexities of the genocide, reducing Palestinians to dehumanized subjects within a broader narrative that privileges Western power and perspectives. Specific linguistic choices, such as references to Gaza’s population density and restricted mobility, normalize suffering and present the crisis as an inherent consequence of cultural or regional factors rather than systemic oppression. Additionally, the essay critiques the use of humanitarian language, which reinforces perceptions of Palestinians asdependent on international aid while simultaneously undermining their agency and self-determination. These portrayals align with dominant ideological narratives that justify foreign intervention, perpetuate power imbalances, and frame the Israeli-Palestinian conflict in ways that uphold existing global hierarchies. By deconstructing these framing strategies, the essay highlights the role of even the ‘liberal’ media in reinforcing Orientalist ideologies and calls for are imagining of journalistic practices to promote justice, equity, and agency. This analysis contributes to the ongoing critique of media representations of marginalized groups and underscores the need for narratives that challenge stereotypes and promote a deeper understanding of the conflict’s root causes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".