{"id":"W4409333029","doi":"10.2139/ssrn.5190273","title":"Artificial Intelligence’s Impact on Legal Journals","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Political science; Psychology; Engineering ethics; Engineering","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":["sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007243085,0.000254186,0.000311495,0.000481083,0.001852804,0.0006102989,0.0009234303,0.0002175143,0.0009185873],"category_scores_gemma":[0.00101276,0.000211695,0.0004037317,0.001044555,0.0004457812,0.0005555385,0.00004816156,0.00319996,0.000451094],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003509407,"about_ca_system_score_gemma":0.009052859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002147178,"about_ca_topic_score_gemma":0.01221658,"domain_scores_codex":[0.9940292,0.0005023991,0.0007483336,0.0003068569,0.0008103517,0.003602816],"domain_scores_gemma":[0.9985709,0.0003622858,0.0002537873,0.0002495495,0.000326809,0.0002366527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001197834,0.0001017062,0.0003495493,8.5164e-7,0.000101284,0.000007992841,0.0007668706,0.0004733448,0.0001038513,0.7926602,0.0004098366,0.2049047],"study_design_scores_gemma":[0.00002663557,0.0003105626,0.0001052747,0.00006292677,0.00003490795,0.00003615832,0.01546908,0.0001198127,0.001164253,0.9640874,0.01835753,0.0002254158],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5293785,0.005110924,0.1732571,0.03971203,0.006214007,0.0009308191,0.00001004499,0.0003443843,0.2450421],"genre_scores_gemma":[0.9894384,0.002981511,0.00005420278,0.0004830363,0.001466932,0.00000596435,6.270783e-7,0.00002026054,0.005549021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4600599,"threshold_uncertainty_score":0.9999947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04099027045314768,"score_gpt":0.4139990059479708,"score_spread":0.3730087354948231,"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."}}