{"id":"W4411635343","doi":"10.1016/j.dss.2025.114499","title":"Impact of categorization autonomy on effective use and adoption intentions","year":2025,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ted Rogers Centre for Heart Research","funders":"","keywords":"Categorization; Autonomy; Psychology; Computer science; Political science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774562,0.0001807879,0.0004355722,0.001439704,0.0001515114,0.0002548858,0.000331862,0.0002442168,0.0001257442],"category_scores_gemma":[0.002355949,0.0001268579,0.0001968092,0.001272338,0.0001155226,0.000494018,0.0001257616,0.0001784346,0.0001416267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764601,"about_ca_system_score_gemma":0.0002036594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000110491,"about_ca_topic_score_gemma":0.000013522,"domain_scores_codex":[0.9973029,0.0002161224,0.000972169,0.0005219277,0.0008051137,0.0001817469],"domain_scores_gemma":[0.9964599,0.001424134,0.0004040998,0.0007405507,0.0008826981,0.00008863588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001467172,0.0001850475,0.9156775,0.000006070217,0.00004915559,0.00000697623,0.0001633725,0.0004649358,0.0009732563,0.01850267,0.009393476,0.05443083],"study_design_scores_gemma":[0.0006153374,0.0003146452,0.9906133,0.00008227173,0.00002672373,0.00001682624,0.0003599481,0.001491076,0.0001209929,0.002438154,0.003808453,0.0001122597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8590596,0.00003644737,0.138178,0.00006438758,0.0008917536,0.0006088999,0.00004656446,0.00008766293,0.001026686],"genre_scores_gemma":[0.9975851,0.00001006231,0.000314651,0.00002841421,0.00001373146,0.00005822691,0.00001781127,0.000008908598,0.001963146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1385254,"threshold_uncertainty_score":0.5173116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07159805148784999,"score_gpt":0.4032096445829803,"score_spread":0.3316115930951303,"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."}}