{"id":"W7104469335","doi":"10.71781/13902","title":"Ben fin, très drôle, vraiment cool : analyse variationniste de l’intensification d’adjectifs dans le cinéma québécois des années 2000 à 2020","year":2024,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Syntax, Semantics, Linguistic Variation","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"French; Statistical analysis; Context (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161882,0.0003583425,0.0001604673,0.001562053,0.002208462,0.002734833,0.0003197101,0.0004967537,0.006435123],"category_scores_gemma":[0.004081817,0.0002533433,0.0001757318,0.00159686,0.001561163,0.001077169,0.0005366972,0.0008568068,0.0003573895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007671434,"about_ca_system_score_gemma":0.003595669,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8049457,"about_ca_topic_score_gemma":0.889416,"domain_scores_codex":[0.9995636,0.0001144561,0.00001150743,0.00009727911,0.0001426312,0.00007037007],"domain_scores_gemma":[0.9974625,0.00118656,0.0002261191,0.00006492312,0.0009223862,0.0001374991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005703382,0.00009371913,0.4216356,0.000639286,0.0002430279,0.001076711,0.3484703,0.001580372,0.03662487,0.02447509,0.01730525,0.1472854],"study_design_scores_gemma":[0.000006439407,0.0000371653,0.8848089,0.0002092744,0.00005739863,0.0001357888,0.06986222,0.001289926,0.002046617,0.0007956963,0.04069804,0.00005246319],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551617,0.001251045,0.002397944,0.00181896,0.00004291779,0.00004502682,0.00107824,0.00002599682,0.03817821],"genre_scores_gemma":[0.9801902,0.0005533019,0.001308055,0.0001824253,0.00001396999,0.00004037554,0.0004190052,0.00003424942,0.01725845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1950543,"threshold_uncertainty_score":0.392406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072368751015492,"score_gpt":0.2764899378544296,"score_spread":0.2457662503442747,"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."}}