{"id":"W4385980932","doi":"10.1017/cls.2023.14","title":"Accès à la justice et inclusion numérique : au-delà des enjeux technologiques","year":2023,"lang":"fr","type":"article","venue":"Canadian Journal of Law and Society / Revue Canadienne Droit et Société","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Humanities; Political science; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.006241421,0.0003890674,0.0006714381,0.0001824828,0.003669238,0.0003010847,0.0009567104,0.001092262,0.0002670353],"category_scores_gemma":[0.001709507,0.0004534313,0.0005372229,0.001143114,0.005167571,0.001081913,0.000263205,0.001573291,0.00002861099],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003366075,"about_ca_system_score_gemma":0.00569377,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4373749,"about_ca_topic_score_gemma":0.9534651,"domain_scores_codex":[0.9960527,0.0007272436,0.0008706853,0.0004348437,0.0002669995,0.00164755],"domain_scores_gemma":[0.9947959,0.001325968,0.0005464614,0.0003027877,0.0008429306,0.002185998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004229486,0.00001701797,0.000234891,0.0001854782,0.0001023878,0.0003916481,0.2915885,0.0001124198,0.00005197156,0.6879367,0.003204216,0.01617058],"study_design_scores_gemma":[0.0001745625,0.0002115654,0.000304228,0.0008574627,0.0001577279,0.0002473233,0.2696898,0.0003991839,0.0001026508,0.3142508,0.413107,0.0004976958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8239976,0.006153804,0.0006378077,0.06664512,0.003982151,0.0004020806,0.0002290061,0.0001044431,0.09784794],"genre_scores_gemma":[0.9739969,0.01087443,0.001090441,0.004749948,0.001036794,0.00001063652,0.00001168941,0.00007055391,0.008158644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5160902,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04491670149482756,"score_gpt":0.3315890010723282,"score_spread":0.2866722995775006,"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."}}