{"id":"W2038270171","doi":"10.3758/bf03200475","title":"Analogy use in naturalistic settings: The influence of audience, emotion, and goals","year":2001,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":163,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Analogy; Connotation; Psychology; Selection (genetic algorithm); Analogical reasoning; Emotionality; Cognitive psychology; Politics; Referendum; Social psychology; Cognitive science; Epistemology; Linguistics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004021906,0.000113616,0.0001620486,0.0001590121,0.00008255384,0.00002428797,0.00008995444,0.0000941207,0.0003066399],"category_scores_gemma":[0.0003777696,0.00008737687,0.00003513423,0.0004000645,0.0001942775,0.0002287477,0.00002367145,0.0001755364,0.00005384418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001441616,"about_ca_system_score_gemma":0.00001228789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003029322,"about_ca_topic_score_gemma":0.0002379731,"domain_scores_codex":[0.9988379,0.0002683717,0.0002917598,0.0002550409,0.0001402999,0.0002066247],"domain_scores_gemma":[0.9991772,0.0002778373,0.0001493955,0.0001833913,0.0001775181,0.00003464467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003075094,0.002812884,0.3190936,0.000755381,0.0007979808,0.001579526,0.1198478,0.0005629842,0.1724501,0.02100112,0.008269193,0.3497544],"study_design_scores_gemma":[0.0009186247,0.00009417158,0.9875441,0.0001130505,0.00009205269,0.0001589669,0.004249365,0.00003657097,0.0005435877,0.005801058,0.0002932845,0.0001552021],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99389,0.000310817,0.000101866,0.0001987102,0.0001679879,0.0002632474,0.00002056162,0.00002902955,0.005017717],"genre_scores_gemma":[0.998179,0.00009281267,0.00003857823,0.001140505,0.00006993845,0.00003826502,0.00005014041,0.000009356982,0.0003814416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6684505,"threshold_uncertainty_score":0.3563125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028956550605169,"score_gpt":0.2879116578851685,"score_spread":0.2676220923791168,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}