{"id":"W4252666514","doi":"10.32920/ryerson.14644449","title":"Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Dalhousie University","funders":"","keywords":"Recommender system; Computer science; Collaborative filtering; Analytics; Context (archaeology); Similarity (geometry); Object (grammar); World Wide Web; Human–computer interaction; Information retrieval; Data science; Multimedia; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001326363,0.0002252229,0.0004100077,0.0001816863,0.000300974,0.0005161444,0.0007268235,0.0002336643,0.0006265091],"category_scores_gemma":[0.0004379353,0.0001228495,0.0003289669,0.0007820257,0.0003975686,0.0001539777,0.0007202723,0.001086754,0.0001090848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489416,"about_ca_system_score_gemma":0.0001298185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008145705,"about_ca_topic_score_gemma":0.0000959918,"domain_scores_codex":[0.9961898,0.001207291,0.0005939343,0.0006506513,0.001033165,0.0003251731],"domain_scores_gemma":[0.9981044,0.00009400231,0.000273898,0.0007937546,0.0006403167,0.00009362594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001693537,0.001392489,0.001915192,0.001571865,0.0003380212,0.000101647,0.05721746,0.1388053,0.5936633,0.0596654,0.1399941,0.005165836],"study_design_scores_gemma":[0.001125248,0.0002657334,0.00150211,0.001594993,0.0005855548,0.0002497553,0.4012601,0.5261279,0.04486015,0.001397279,0.01911789,0.001913219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647142,0.000309882,0.001519015,0.004847128,0.0009621539,0.0006159661,0.00003469013,0.0001208642,0.02687613],"genre_scores_gemma":[0.9820786,0.0007853832,0.0002706192,0.0002449318,0.0001248739,0.00004256859,0.0000564495,0.00001557042,0.01638096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5488032,"threshold_uncertainty_score":0.6859835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3080797062044025,"score_gpt":0.4272026156603138,"score_spread":0.1191229094559113,"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."}}