{"id":"W2971947976","doi":"10.1515/sem-2018-0110","title":"Raw data or hypersymbols? Meaning-making with digital data, between discursive processes and machinic procedures","year":2019,"lang":"en","type":"article","venue":"Semiotica","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":"Semiotics; Epistemology; Big data; Sociology; Rhetorical question; Meaning (existential); Value (mathematics); Social semiotics; Context (archaeology); Computer science; Data science; Linguistics; Philosophy","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":[0.02101204,0.0006195427,0.0006595609,0.004301552,0.004230696,0.01749685,0.001453057,0.002586846,0.003154706],"category_scores_gemma":[0.03255743,0.0004403547,0.0005336718,0.003607139,0.102275,0.02599964,0.006477621,0.004080079,0.0007106304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004328231,"about_ca_system_score_gemma":0.003193102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309005,"about_ca_topic_score_gemma":0.0009738642,"domain_scores_codex":[0.9784071,0.01652056,0.0007912944,0.001302622,0.002603968,0.0003744266],"domain_scores_gemma":[0.9663176,0.02577649,0.001590412,0.004013271,0.001764308,0.0005379252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000677411,0.000003060946,0.00005392822,0.00003064306,0.000002168326,0.00002458178,0.005478575,0.00006384018,0.00005579392,0.9911838,0.0004703322,0.002626632],"study_design_scores_gemma":[0.000006678973,0.000006377876,0.00007341761,0.0001506117,0.000002759326,0.00004938266,0.004202722,0.0004674369,0.000245505,0.9642066,0.03057888,0.00000964044],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04665695,0.0197621,0.5218757,0.1003306,0.002357462,0.000307399,0.0003574084,0.0002874773,0.3080649],"genre_scores_gemma":[0.9027799,0.005458337,0.07587735,0.0034355,0.001058542,0.0004347418,0.0001248799,0.0001885553,0.01064217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02101204,"threshold_uncertainty_score":0.1111236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09072691165092146,"score_gpt":0.3995020730323609,"score_spread":0.3087751613814395,"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."}}