spec-resistance corpus: 30-model, 627,491-trial dataset for "Large language models that perfectly evaluate products systematically refuse to recommend the best one"

Primary computational corpus for the manuscript "Large language models that perfectly evaluate products systematically refuse to recommend the best one" (Affonso, Spears School of Business, Oklahoma State University). Contains 627,491 controlled product-recommendation trials across 30 frontier language models from seven developers, tested in 32 experimental conditions spanning 20 consumer product categories. The 18-model subset (382,679 trials, the original v1 corpus) and the 12-cell subset (244,812 trials) are derivable by filtering on the model_key column. Code, smaller derived data, and the four behavioural studies' anonymised CSVs live in the companion GitHub repository: https://github.com/FelipeMAffonso/spec-resistance Interactive online explorer: https://felipemaffonso.github.io/spec-resistance-companion/ Each row of the CSV is a single product-recommendation trial. The 77 columns include trial metadata (model_key, condition, assortment_id, paraphrase_idx), the five-product display, the model's verbatim response, the extracted choice letter, the optimal letter and specification-optimal flag, and matched-model judge labels (judge_coherence, judge_specification_acknowledgment, judge_brand_reasoning). SHA-256 hashes are recorded in hashes.json in the GitHub repository for integrity verification.