{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006232159,0.0001254836,0.0002454487,0.00005423066,0.0003048675,0.0003647853,0.0008841199,0.00004913147,0.0001097595],"category_scores_gemma":[0.002325981,0.0000820395,0.00001447099,0.0004475803,0.0002293825,0.001065773,0.0005197728,0.0001300833,0.00002716753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750892,"about_ca_system_score_gemma":0.000624731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009489681,"about_ca_topic_score_gemma":0.00138905,"domain_scores_codex":[0.9984823,0.0001011798,0.0001688386,0.0005516994,0.0004463822,0.0002495953],"domain_scores_gemma":[0.9978926,0.001293113,0.0001110167,0.0004958845,0.0001097308,0.00009765324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002466781,0.0002039317,0.8476956,0.0007637524,0.0008771998,0.00002983262,0.02694143,0.0001396483,0.00001620352,0.01700464,0.002552178,0.1035288],"study_design_scores_gemma":[0.006658952,0.001933908,0.3035598,0.00577644,0.005048643,0.0001452826,0.1857026,0.03482226,0.0001077913,0.1451686,0.3039879,0.007087693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8351977,0.001703721,0.01941612,0.01287779,0.0003658316,0.002021116,0.001094648,0.0004919532,0.1268311],"genre_scores_gemma":[0.9940754,0.00002608717,0.004180145,0.00007622581,0.0002563988,0.000001533674,0.0003027773,0.00001320554,0.001068279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5441358,"threshold_uncertainty_score":0.3517633,"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."}}