{"id":"W2626868888","doi":"10.1111/radm.12275","title":"The effects of the chief technology officer and firm and industry R&D intensity on organizational performance","year":2017,"lang":"en","type":"article","venue":"R and D Management","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Contingency; Compensation (psychology); Business; Contingency theory; Industrial organization; Officer; Power (physics); Economics; Microeconomics; Monetary economics; Management","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":[],"consensus_categories":[],"category_scores_codex":[0.00008860181,0.00007822777,0.00007978119,0.00003164882,0.0006241729,0.000115038,0.0001604772,0.00004943968,0.000002729576],"category_scores_gemma":[0.00006354618,0.00004665689,0.000008872744,0.00009014848,0.0001881879,0.0001635983,0.0004508332,0.0001055187,0.000004436697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004634507,"about_ca_system_score_gemma":0.000002964542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003333151,"about_ca_topic_score_gemma":0.00004153879,"domain_scores_codex":[0.9996006,0.000001823148,0.00007565592,0.0001360747,0.00009149568,0.0000942949],"domain_scores_gemma":[0.9995008,0.0000150359,0.0001692654,0.0002698817,0.00004091191,0.000004116212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003912197,0.00002914421,0.8977529,0.0004336865,0.00002981176,0.000002489607,0.00001647892,0.00000318122,0.00005091508,0.07448269,0.0037828,0.02337672],"study_design_scores_gemma":[0.0003158232,0.00001007142,0.953185,0.000116295,0.00002480076,0.000001005879,0.00001829094,0.00008563195,0.00007370838,0.001466975,0.0446423,0.00006008142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934499,0.0001456427,0.00001315304,0.002997989,0.0001859792,0.000187111,7.035373e-7,0.00001155754,0.003007947],"genre_scores_gemma":[0.9975681,0.0005413903,0.00001069478,0.0005509892,0.00007359451,0.000006668581,5.949303e-7,0.000005349179,0.001242642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07301572,"threshold_uncertainty_score":0.4800696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00729663095368025,"score_gpt":0.1877497369990052,"score_spread":0.180453106045325,"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."}}