{"id":"W3033545325","doi":"10.4000/sdt.30182","title":"Match Point. Spécialisation et différenciation sur le marché du travail des attachées de presse de cinéma","year":2020,"lang":"fr","type":"article","venue":"Sociologie du Travail","topic":"Education, sociology, and vocational training","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Musée de la Civilisation","funders":"","keywords":"Humanities; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007468547,0.0003720559,0.0004638631,0.00006143251,0.001234201,0.0001517805,0.0006187962,0.000843836,0.0004426219],"category_scores_gemma":[0.006921937,0.0003876876,0.0002684716,0.0004092676,0.003790312,0.0006983434,0.00006667647,0.0006999615,0.0001595082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589205,"about_ca_system_score_gemma":0.002209578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004358173,"about_ca_topic_score_gemma":0.0009508816,"domain_scores_codex":[0.9924225,0.004928376,0.000616216,0.0006433019,0.0004010905,0.0009885597],"domain_scores_gemma":[0.9952744,0.003369974,0.0004063847,0.0002032459,0.0003774586,0.0003685553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0000497922,0.0005577308,0.2399648,0.0003040364,0.0002093612,0.000006858682,0.5431938,0.0003664003,0.001210993,0.0906565,0.1023023,0.02117736],"study_design_scores_gemma":[0.0009948405,0.0002655883,0.7173215,0.00008983661,0.0001372157,0.000008116221,0.2157704,0.001015843,0.0001434112,0.02749027,0.03618008,0.0005829165],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7400694,0.008609095,0.005620568,0.2360422,0.001584833,0.0004887318,0.0001028956,0.0002985774,0.007183692],"genre_scores_gemma":[0.956589,0.02521962,0.003736438,0.005291589,0.006057686,0.0001191117,0.0002148519,0.00005329909,0.002718341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4773567,"threshold_uncertainty_score":0.9998575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2703465508427272,"score_gpt":0.4026596958039254,"score_spread":0.1323131449611982,"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."}}