{"id":"W2803792035","doi":"10.1111/jbfa.12318","title":"Litigation risk: Measurement and impact on M&A transaction terms","year":2018,"lang":"en","type":"article","venue":"Journal of Business Finance &amp Accounting","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Shareholder; Business; Database transaction; Actuarial science; Proxy (statistics); Litigation risk analysis; Payment; Sample (material); Scope (computer science); Robustness (evolution); Accounting; Risk management; Finance; Corporate governance; Computer science","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":[0.005611092,0.0002632313,0.0003702064,0.003764273,0.0004283877,0.002806362,0.0008503023,0.000760688,0.004389435],"category_scores_gemma":[0.05231157,0.0001360051,0.0004451686,0.005238921,0.001070044,0.001834712,0.002157881,0.001439238,0.0005260462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145475,"about_ca_system_score_gemma":0.0005018627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002312742,"about_ca_topic_score_gemma":0.002445754,"domain_scores_codex":[0.9909734,0.003174356,0.001296036,0.000797058,0.00332274,0.0004363437],"domain_scores_gemma":[0.8399079,0.06607816,0.0810679,0.005401952,0.004758444,0.00278563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001221346,0.0001285628,0.9838815,0.00004116353,0.0001268355,0.0001391255,0.00023616,0.002356424,0.0005546464,0.002600803,0.0004890265,0.00932355],"study_design_scores_gemma":[0.000006909745,0.0002595841,0.9848619,0.00003546766,0.00005387382,0.0002979413,0.0003692164,0.009923726,0.001093613,0.00219224,0.0008778374,0.00002765266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865825,0.000518491,0.004371457,0.0003163248,0.0000158661,0.00006377725,0.001488674,0.00003779272,0.006605112],"genre_scores_gemma":[0.9988143,0.00004864443,0.0004326501,0.0000152732,0.00002088995,0.00001156479,0.0003362039,0.000002804376,0.0003177859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005611092,"threshold_uncertainty_score":0.02967465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03205230289954022,"score_gpt":0.2398293885541293,"score_spread":0.2077770856545891,"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."}}