{"id":"W4416829564","doi":"10.2139/ssrn.5813722","title":"Getting Personal: Individualized and User-Searchable Readability Results for a Large Corpus of Canadian Adjudicative Decisions","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Readability; Tribunal; Subject (documents); Resource (disambiguation); Empirical research; Word (group theory)","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009098463,0.0002630684,0.0005250315,0.000900749,0.0005711956,0.0001872432,0.001389833,0.0003191733,0.000002050769],"category_scores_gemma":[0.002972392,0.0002517038,0.0002224854,0.0006604657,0.0001036335,0.0002307197,0.0006280182,0.002626174,9.94606e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507244,"about_ca_system_score_gemma":0.01895286,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02430789,"about_ca_topic_score_gemma":0.2525176,"domain_scores_codex":[0.9955112,0.000394256,0.0008103289,0.0007963754,0.0004521845,0.002035656],"domain_scores_gemma":[0.9963107,0.001282504,0.0006368493,0.0007884663,0.0006906763,0.0002908371],"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":[0.0003177973,0.0002754923,0.002963982,0.0001919924,0.0005778125,0.000002568984,0.008693243,0.00006844488,0.0001030637,0.8132148,0.001048182,0.1725426],"study_design_scores_gemma":[0.001615549,0.0003652821,0.008749837,0.0004113209,0.0001008186,0.00008045878,0.001523901,0.01043869,0.0002808731,0.9550629,0.02089095,0.000479479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09936053,0.005717009,0.8768884,0.01268855,0.0004248677,0.002017489,0.001722254,0.00008805802,0.00109284],"genre_scores_gemma":[0.9660709,0.004233483,0.02833448,0.0001373591,0.00009495482,0.00009667987,0.000111336,0.00001665072,0.0009041834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8667104,"threshold_uncertainty_score":0.9999935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315900626650401,"score_gpt":0.3049321644477418,"score_spread":0.2717731581812378,"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."}}