{"id":"W3124891938","doi":"10.1093/rof/rfn001","title":"Option Compensation and Industry Competition","year":2008,"lang":"en","type":"article","venue":"European Finance Review","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada; University Grants Committee","keywords":"Disadvantage; Compensation (psychology); Flexibility (engineering); Business; Competition (biology); Industrial organization; Corporate governance; Stock (firearms); Executive compensation; Stock options; Economics; Finance; Computer science; Engineering; Management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003044936,0.0001360175,0.0003423443,0.0011003,0.0006285995,0.00235244,0.0004441375,0.001295173,0.009194888],"category_scores_gemma":[0.008130159,0.00007145294,0.0002172517,0.0009092872,0.002096439,0.001405959,0.001100693,0.0009359216,0.0003700783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001795496,"about_ca_system_score_gemma":0.001448828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001790919,"about_ca_topic_score_gemma":0.002355646,"domain_scores_codex":[0.9967701,0.001330002,0.0001202307,0.0002641531,0.0009315276,0.0005839182],"domain_scores_gemma":[0.9909979,0.003936828,0.002612116,0.0003116955,0.001286122,0.0008552717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001019897,0.00008214836,0.01330946,0.0001228609,0.00004592741,0.0002211521,0.0003443334,0.006907467,0.001232816,0.9182125,0.005863054,0.05355637],"study_design_scores_gemma":[0.0001049881,0.0002837678,0.05703298,0.0003147035,0.00005367537,0.0005928177,0.0008866582,0.01839834,0.001545234,0.8548687,0.06584127,0.00007690753],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5059458,0.01842551,0.02076975,0.02389417,0.0002428602,0.00005614292,0.0001409341,0.00005456207,0.4304703],"genre_scores_gemma":[0.9949164,0.0006108831,0.000336677,0.0003660016,0.00008596974,0.00000513373,0.00001550588,0.000002336305,0.003661032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009194888,"threshold_uncertainty_score":0.03075993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03989175459154792,"score_gpt":0.2183308610724469,"score_spread":0.178439106480899,"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."}}