{"id":"W3206217212","doi":"10.4000/books.pup.47878","title":"Attributions multiples, anonymat des textes normatifs : outils et pistes pour une enquête","year":2016,"lang":"fr","type":"book-chapter","venue":"Presses universitaires de Provence eBooks","topic":"Linguistics and Discourse Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bibliothèque et Archives nationales du Québec","funders":"","keywords":"Political science; 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":[0.01330038,0.0007876685,0.0006160866,0.005050153,0.006104313,0.01222932,0.001154009,0.001498551,0.007002386],"category_scores_gemma":[0.03624826,0.0005771016,0.0006194185,0.0075824,0.01210325,0.02177276,0.006341094,0.004417852,0.001932855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005336975,"about_ca_system_score_gemma":0.003113549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004860899,"about_ca_topic_score_gemma":0.008338995,"domain_scores_codex":[0.9814361,0.01103576,0.001054383,0.001515355,0.00458074,0.0003776874],"domain_scores_gemma":[0.9774659,0.01282387,0.001375273,0.003496344,0.004332391,0.0005061374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.000110268,0.00003291484,0.002474218,0.0005741408,0.00003449956,0.0004797902,0.1302799,0.0004779488,0.001981526,0.687537,0.03915816,0.1368597],"study_design_scores_gemma":[0.00001005634,0.00002334653,0.003236918,0.0009281181,0.00002994168,0.001034559,0.05421862,0.00191165,0.002224155,0.098377,0.8379155,0.00009011579],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1396862,0.04869217,0.4108229,0.07298274,0.0164388,0.0003244443,0.002287996,0.001790905,0.3069739],"genre_scores_gemma":[0.7636582,0.02077275,0.09541067,0.002108487,0.005683916,0.0004651157,0.001948063,0.002253206,0.1076996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01330038,"threshold_uncertainty_score":0.07033992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03993474338386814,"score_gpt":0.2537825680486829,"score_spread":0.2138478246648147,"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."}}