{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001047702,0.0003205983,0.0002180528,0.001453393,0.006089902,0.007053108,0.0006704195,0.001531129,0.05368044],"category_scores_gemma":[0.004457071,0.0003378129,0.0003204113,0.001727604,0.005987089,0.004706534,0.003955877,0.001960769,0.005548736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004270513,"about_ca_system_score_gemma":0.002702541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02818247,"about_ca_topic_score_gemma":0.04467978,"domain_scores_codex":[0.9987254,0.0003123,0.00003827094,0.0003524089,0.0003028833,0.00026878],"domain_scores_gemma":[0.9980311,0.0004399653,0.000302279,0.0002151009,0.0004538706,0.0005576697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002868164,0.00007603726,0.02310024,0.0002128734,0.000029042,0.001118236,0.1496783,0.0004501318,0.002941745,0.7062678,0.02519324,0.09064556],"study_design_scores_gemma":[0.00002977012,0.0001586449,0.1148861,0.0003299238,0.00002886214,0.0007447096,0.1215388,0.0008833943,0.00104554,0.05457347,0.7057225,0.00005833227],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2945431,0.001689188,0.01140633,0.01114901,0.0006504245,0.00008159329,0.0003992243,0.0001951021,0.6798859],"genre_scores_gemma":[0.794762,0.0004284652,0.002474986,0.000355066,0.0001228153,0.00004897357,0.0001417746,0.0001037121,0.2015621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05368044,"threshold_uncertainty_score":0.179579,"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."}}