{"id":"W3141202084","doi":"","title":"Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires","year":2010,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Exploit; Stock (firearms); Tacit knowledge; Sample (material); Variation (astronomy); Citation; Control (management); Business; Industrial organization; Demographic economics; Economics; Econometrics; Computer science; Knowledge management; Management; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004483657,0.0002300408,0.0004864172,0.001871168,0.0009037469,0.00295291,0.0008452414,0.001268425,0.005239217],"category_scores_gemma":[0.02279583,0.0002088632,0.0005303152,0.001839109,0.001215384,0.003423485,0.00153517,0.0006476355,0.000694463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007004457,"about_ca_system_score_gemma":0.000795899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003352253,"about_ca_topic_score_gemma":0.005803728,"domain_scores_codex":[0.9987797,0.0003992315,0.00007644205,0.0002178533,0.0002232793,0.0003034805],"domain_scores_gemma":[0.9782588,0.0139083,0.005207961,0.001079016,0.0005098247,0.001036064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003558138,0.0007938662,0.7839993,0.0002557837,0.0002081128,0.0005186843,0.005675305,0.004090054,0.004203828,0.01972542,0.001445577,0.1787282],"study_design_scores_gemma":[0.00006700923,0.0006468574,0.9552169,0.0001300856,0.0002230746,0.0005139663,0.006562952,0.008057675,0.001725609,0.02019043,0.006604212,0.00006116839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896388,0.0005053073,0.001569746,0.0003569707,0.000006802747,0.00003239447,0.00004842572,0.00001599674,0.007825636],"genre_scores_gemma":[0.9973315,0.000320188,0.0004716231,0.00005932391,0.00001601746,0.00001497221,0.00004280699,0.000002537513,0.001741044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005239217,"threshold_uncertainty_score":0.0237121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4783266768086222,"score_gpt":0.4678448511814666,"score_spread":0.01048182562715566,"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."}}