{"id":"W4415464860","doi":"10.52358/mm.vi21.463","title":"Différencier les enseignements avec et par le numérique ou l’équité à l’épreuve des contraintes","year":2025,"lang":"en","type":"article","venue":"Médiations et médiatisations","topic":"Education, sociology, and vocational training","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bad debt; Context (archaeology); Ethnic community","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009486354,0.001049018,0.0009872904,0.001947297,0.003061565,0.009979504,0.002549757,0.003408328,0.019717],"category_scores_gemma":[0.04076205,0.0006716864,0.001216109,0.002023982,0.011557,0.01512956,0.007031198,0.004819405,0.00216624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004736594,"about_ca_system_score_gemma":0.005638475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008435267,"about_ca_topic_score_gemma":0.01029618,"domain_scores_codex":[0.9883301,0.006055976,0.0006956605,0.001545902,0.002817322,0.0005550131],"domain_scores_gemma":[0.9747872,0.01678552,0.002371357,0.002410665,0.003037428,0.0006078241],"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":[0.00005999799,0.00003792994,0.002841333,0.0002434493,0.00002666369,0.0002402455,0.0127305,0.002153706,0.0007602881,0.9474184,0.0009232146,0.03256429],"study_design_scores_gemma":[0.00003340776,0.0001269654,0.004182816,0.001200626,0.0000720267,0.0007833287,0.01831278,0.01089853,0.002672866,0.7824602,0.1791334,0.0001231527],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1618689,0.007918925,0.5268245,0.01773154,0.0009620857,0.0002072677,0.0002818028,0.0003552771,0.2838497],"genre_scores_gemma":[0.8011636,0.004632509,0.1452186,0.001261509,0.0002595611,0.0003610467,0.0002221052,0.0003510318,0.04653012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.019717,"threshold_uncertainty_score":0.06595999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1757107430746571,"score_gpt":0.4327438915999264,"score_spread":0.2570331485252693,"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."}}