{"id":"W4393230858","doi":"10.1111/caje.12705","title":"Pause artificial intelligence research? Understanding AI policy challenges","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Ambiguity; Productivity; Economics; Humanity; Computer science; Artificial intelligence; Political science; Macroeconomics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004494792,0.0003456386,0.0008446014,0.003768315,0.0003472193,0.0006573033,0.0008364338,0.0003103061,0.0005540259],"category_scores_gemma":[0.0008592331,0.0004624395,0.0003386915,0.0004696202,0.0004289648,0.001199481,0.00004903694,0.001119369,0.0005344726],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006819747,"about_ca_system_score_gemma":0.003044048,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09988869,"about_ca_topic_score_gemma":0.8868099,"domain_scores_codex":[0.9961128,0.00008847706,0.001630584,0.0008072163,0.000003285352,0.001357617],"domain_scores_gemma":[0.9965423,0.0003853109,0.0004509786,0.0006082104,0.0001341126,0.001879048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002732757,0.00001261621,0.0005683864,0.0001025277,0.0001679045,0.0001517246,0.002418732,0.0007256301,0.000003556849,0.9889221,0.0007997311,0.006099756],"study_design_scores_gemma":[0.00008494189,0.0002564899,0.0001180606,0.0001205708,0.000009703272,0.0003031607,0.001173749,0.002397795,0.00007090185,0.9306697,0.06436918,0.000425786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9026583,0.01412326,0.002230476,0.05557197,0.00541983,0.0003753151,0.0005490996,0.00003070874,0.01904103],"genre_scores_gemma":[0.9938044,0.002003789,0.000204038,0.0004061999,0.003022668,0.00001560046,0.000008661988,0.0000942105,0.0004404275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7869213,"threshold_uncertainty_score":0.9997827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5779821891415509,"score_gpt":0.2727495388817243,"score_spread":0.3052326502598265,"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."}}