{"id":"W4211222676","doi":"10.3917/inso.205.074","title":"L’indice de fragilité numérique : les données comme levier pour comprendre les exclus du numérique","year":2022,"lang":"fr","type":"article","venue":"Informations sociales","topic":"Aging, Elder Care, and Social Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Emploi et de la Solidarité Sociale (Québec)","funders":"","keywords":"Humanities; Political science; Philosophy","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":[0.01374701,0.0009212971,0.0008883871,0.006880447,0.001211934,0.003465961,0.001719124,0.001116371,0.007834696],"category_scores_gemma":[0.05086432,0.0003972599,0.001531486,0.005445849,0.001745026,0.0028697,0.00212529,0.001428076,0.001226912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003377554,"about_ca_system_score_gemma":0.004710684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03189266,"about_ca_topic_score_gemma":0.04587,"domain_scores_codex":[0.9919947,0.003432504,0.001008293,0.0006845461,0.002554518,0.0003254828],"domain_scores_gemma":[0.9538067,0.02966201,0.005078907,0.002081634,0.008627543,0.0007431983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007821035,0.0002742251,0.5809576,0.005604216,0.0008275696,0.0002044428,0.02529046,0.00109466,0.001875029,0.01820928,0.009750874,0.3551295],"study_design_scores_gemma":[0.00006146714,0.0006367117,0.848254,0.004969961,0.0007054356,0.0007182335,0.01615423,0.001899038,0.004307786,0.007992201,0.1140522,0.0002489296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7146657,0.03582248,0.05998465,0.008180602,0.00113494,0.001476976,0.02004425,0.0005291956,0.1581612],"genre_scores_gemma":[0.9114012,0.008768817,0.05766785,0.0008498485,0.0002286211,0.002032899,0.004472879,0.0001263951,0.01445166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03189266,"threshold_uncertainty_score":0.07270199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1137782947455409,"score_gpt":0.3660728570184194,"score_spread":0.2522945622728785,"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."}}