{"id":"W2940685465","doi":"10.17848/wp19-301","title":"Local Job Multipliers in the United States: Variation with Local Characteristics and with High-Tech Shocks","year":2019,"lang":"en","type":"report","venue":"","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pew Charitable Trusts","keywords":"High tech; Quarter (Canadian coin); Economics; Shock (circulatory); Job creation; Demand shock; Econometrics; Population; Lagrange multiplier; Microeconomics; Labour economics; Mathematics; Geography; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008761921,0.0003757548,0.0009703767,0.0006561455,0.00008702256,0.0001901389,0.0002835618,0.0003256455,0.0001270802],"category_scores_gemma":[0.00002125364,0.0002584849,0.00008393801,0.0004467331,0.0001930166,0.0001212912,0.00005514126,0.0004735127,0.00005924608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004131258,"about_ca_system_score_gemma":0.0002108079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07648687,"about_ca_topic_score_gemma":0.008982785,"domain_scores_codex":[0.9979544,0.00002478047,0.000881121,0.0006876028,0.000135826,0.000316274],"domain_scores_gemma":[0.9983317,0.0001463361,0.0008021633,0.0004887528,0.0001525992,0.00007839477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001173995,0.0007924556,0.6232598,0.001101895,0.004492498,0.0002717608,0.003920989,0.09617896,0.000001039233,0.2539646,0.006682264,0.008159739],"study_design_scores_gemma":[0.003026152,0.0009791267,0.4710627,0.0003236098,0.0002927364,0.0001148432,0.001783691,0.3456356,0.000001961985,0.005425464,0.169218,0.0021361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5155824,0.0003600961,0.4501397,0.003068553,0.0003573393,0.001253006,0.00113083,0.00005155889,0.02805645],"genre_scores_gemma":[0.9914929,0.00307175,0.0003687378,0.0005235462,0.00009874749,0.00004928995,0.001605471,0.00005821788,0.002731291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4759105,"threshold_uncertainty_score":0.9999867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225430384181649,"score_gpt":0.2059198534787226,"score_spread":0.1833768150605577,"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."}}