{"id":"W4296353126","doi":"10.20944/preprints202209.0259.v1","title":"Social Media Platforms: Trading with Prediction Error Minimization for Your Attention","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Affordance; Social media; Salient; Salience (neuroscience); Inference; Computer science; Internet privacy; Human–computer interaction; Cognitive science; Cognitive psychology; Psychology; Artificial intelligence; World Wide Web","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00217926,0.0002377702,0.0003776904,0.0002693769,0.001295096,0.00006206285,0.0004354136,0.000263159,0.001451804],"category_scores_gemma":[0.0005873198,0.000244235,0.0003338528,0.0004095568,0.0001470367,0.000231889,0.0003493713,0.000477233,0.00002022268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004857052,"about_ca_system_score_gemma":0.0004308159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002799393,"about_ca_topic_score_gemma":0.000495165,"domain_scores_codex":[0.9972419,0.0002802432,0.0004937614,0.0007587227,0.0008975181,0.0003278755],"domain_scores_gemma":[0.9984418,0.000390762,0.0005356059,0.0002356661,0.0002885922,0.0001076204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006426633,0.0005732356,0.7888524,0.0003669616,0.001141435,0.0000072791,0.1343786,0.01928877,0.0003701516,0.03818848,0.0007432342,0.01544681],"study_design_scores_gemma":[0.00141525,0.00005510986,0.8197804,0.0001259158,0.0009500735,0.000002322974,0.02066503,0.02085146,0.0001510929,0.1097306,0.02533584,0.0009368683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562269,0.00003397538,0.03203659,0.001125583,0.001206562,0.001085184,0.0001115866,0.0002872774,0.007886299],"genre_scores_gemma":[0.9920356,0.00002813875,0.003499964,0.00003595867,0.001282066,0.0005703456,0.0009377078,0.00003560393,0.001574674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1137136,"threshold_uncertainty_score":0.999461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3097375245482143,"score_gpt":0.4439542159530109,"score_spread":0.1342166914047966,"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."}}