{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01805853,0.0009074615,0.0005437531,0.002942435,0.001730283,0.009935498,0.002256641,0.002652425,0.003690808],"category_scores_gemma":[0.03970933,0.0007617536,0.001055319,0.002636112,0.004290198,0.009454122,0.006278854,0.0024196,0.001266364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002033988,"about_ca_system_score_gemma":0.001648256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005284461,"about_ca_topic_score_gemma":0.005778937,"domain_scores_codex":[0.9884989,0.007230997,0.0004872196,0.001397274,0.002084025,0.0003015856],"domain_scores_gemma":[0.967912,0.02052288,0.00126781,0.005905018,0.003316284,0.001075928],"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":[0.0003906006,0.0003577104,0.02362547,0.001638468,0.0003570105,0.001047643,0.06167599,0.005068236,0.0130083,0.1600028,0.01115636,0.7216715],"study_design_scores_gemma":[0.0002765541,0.001573974,0.03146859,0.003101052,0.000593915,0.00391943,0.05068649,0.09921844,0.01733654,0.2990159,0.4921277,0.0006815948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1644336,0.006005952,0.7315931,0.01437713,0.0006300291,0.0006976174,0.0003810951,0.003848887,0.07803252],"genre_scores_gemma":[0.6278981,0.003222258,0.3587901,0.001174661,0.0002142098,0.0002900805,0.0002892314,0.0003706607,0.007750697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01805853,"threshold_uncertainty_score":0.09550375,"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."}}