{"id":"W3128005332","doi":"10.1037/rev0000265","title":"Disentangling contextual diversity: Communicative need as a lexical organizer.","year":2021,"lang":"en","type":"article","venue":"Psychological Review","topic":"Topic Modeling","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Lexicon; PsycINFO; Linguistics; Diversity (politics); Word lists by frequency; Psychology; Context effect; Lexical item; Cognitive psychology; Word (group theory); Computer science; Natural language processing; Sociology; History","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003081128,0.0006808427,0.0006908673,0.004329269,0.001353069,0.003988657,0.0009061465,0.0008520359,0.002222568],"category_scores_gemma":[0.02459005,0.0005406054,0.0008552594,0.002616659,0.003921663,0.01057053,0.005764522,0.001469397,0.0002191863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239241,"about_ca_system_score_gemma":0.0008278042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002190688,"about_ca_topic_score_gemma":0.00252989,"domain_scores_codex":[0.9974953,0.0009393967,0.0001616006,0.0006436257,0.0006234463,0.0001366243],"domain_scores_gemma":[0.9829838,0.01116465,0.00239366,0.001651337,0.0009577349,0.0008487667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001531765,0.0004538932,0.2463736,0.001682106,0.0008566623,0.001303683,0.07994587,0.008100725,0.04023328,0.3047403,0.002178284,0.3125998],"study_design_scores_gemma":[0.0001350162,0.0007086503,0.2815098,0.0004059515,0.000799713,0.001994275,0.02481462,0.06118862,0.008941592,0.6023827,0.01678616,0.0003328171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8464615,0.001542864,0.116122,0.001216545,0.00006308148,0.0001589048,0.0005407021,0.0001514686,0.03374294],"genre_scores_gemma":[0.9882901,0.0001062919,0.01092189,0.00008309578,0.00002653164,0.00007693902,0.0001400935,0.00002972086,0.0003254203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004329269,"threshold_uncertainty_score":0.01629478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1186266797586199,"score_gpt":0.3784142453983887,"score_spread":0.2597875656397688,"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."}}