This study compares how five LLMs, Gemma 3 27B, Qwen 3 32B, Llama 3.3 70B Instruct, Gemini 2.5 Pro and GPT-4.1, complete Palestinian and Israeli profiles across war versus no-war context, age framing and five roles. Across 640 configurations per model and 3,200 baseline profiles, war more often shifts Palestinian profiles toward lower socioeconomic status, survival-oriented occupations and fatigue or injury descriptors, while Israeli profiles remain predominantly middle class and professionally specialised. Adding a warning against harmful assumptions does not correct the pattern consistently: depending on the model it changes pronouns, converges occupations toward student or alters appearance, while socioeconomic differences persist. This is useful evidence of representational disparity and prompt sensitivity, not a measurement of real people. There is no demographic reference or operational fairness target; the design appears to use one generation per cell at temperature 0.7, without intervals or tests. The prompt forces children to have worked and reduces gender and class to closed categories. Its rationale analysis also counts words supplied by the warning itself, while an SAE from another Llama is applied to text rather than target-model activations. No data or code supports regeneration. Official Figure 9 permutes the Llama, Qwen and GPT series relative to Table 1. The paper supports auditing prompt-induced associations, not faithful population simulation or demonstrated fair reasoning.
Research question
How five LLMs represent Palestinian and Israeli identities when generating personas under variations of war, age, and role; how that representation changes when asked to avoid harmful assumptions; and what their textual justifications show about that response to the concept of justice.