{"id":"W4200077347","doi":"10.32920/17114147","title":"Lingo-Entrainment: The Natural Language Surveillance Of Smartphone Users","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Digital Communication and Language","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; York University; University of Toronto","funders":"","keywords":"Context (archaeology); Entrainment (biomusicology); Divestment; Internet privacy; Psychology; Advertising; Computer science; Business; Political science; Aesthetics; History; Law; Art","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648758,0.0001986945,0.0001756903,0.001520118,0.0009568005,0.002486248,0.000391213,0.0006284242,0.001421383],"category_scores_gemma":[0.01286504,0.0001491189,0.0001891753,0.001014696,0.001445162,0.002740872,0.002227647,0.0006458625,0.0004956963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007845479,"about_ca_system_score_gemma":0.0005738176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00814631,"about_ca_topic_score_gemma":0.009499221,"domain_scores_codex":[0.9985429,0.0006706306,0.0001195295,0.0002346521,0.0003127431,0.0001196526],"domain_scores_gemma":[0.9920545,0.004153624,0.001947239,0.0006986437,0.0008694073,0.00027665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003249574,0.00006733707,0.242929,0.0003279136,0.00003749206,0.001256578,0.6430701,0.0001097039,0.0152496,0.002423177,0.002944643,0.09125941],"study_design_scores_gemma":[0.000008140011,0.0002658539,0.5179057,0.0002517833,0.00004510718,0.002515861,0.4237579,0.001913581,0.006277778,0.002549215,0.04437892,0.0001301344],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936196,0.0002534244,0.001116295,0.000581973,0.00001454194,0.00003800226,0.0004526352,0.00004680491,0.003876632],"genre_scores_gemma":[0.9967224,0.000254478,0.001159589,0.00027663,0.00002036075,0.00003787838,0.0003651326,0.00002657924,0.001137108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00814631,"threshold_uncertainty_score":0.0161978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128656211749104,"score_gpt":0.2495936191490264,"score_spread":0.2383070570315353,"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."}}