{"id":"W7060878936","doi":"","title":"Organisational and Technological AI Readiness: Evidence from Canadian Public Administration","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Value (mathematics); Administration (probate law); Technological change; Public sector; Public value; Survey data collection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004857286,0.0003911962,0.0005921157,0.004463902,0.005445725,0.003174994,0.002396108,0.000816272,0.004903707],"category_scores_gemma":[0.02474567,0.0005334543,0.000661433,0.01455777,0.003058518,0.001405086,0.002825236,0.001440589,0.0003336189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05059487,"about_ca_system_score_gemma":0.09449526,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9968523,"about_ca_topic_score_gemma":0.9982426,"domain_scores_codex":[0.9950582,0.0004973048,0.0002479709,0.0004422533,0.002275049,0.001479312],"domain_scores_gemma":[0.9682811,0.00495087,0.006094467,0.001388736,0.01606818,0.003216543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009583693,0.00009713451,0.9644005,0.0003245382,0.0001239557,0.0001083086,0.0124682,0.0001408906,0.00008304149,0.001425036,0.004211542,0.016521],"study_design_scores_gemma":[0.000006576342,0.00001402082,0.9841208,0.0002108583,0.00005827953,0.00001575513,0.01214206,0.0001209318,0.00004243465,0.00009002576,0.003159051,0.00001920646],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726158,0.003066745,0.0002058531,0.00375696,0.0000272748,0.00009630488,0.00462016,0.00001328196,0.01559767],"genre_scores_gemma":[0.9952917,0.002181276,0.000158429,0.0003796838,0.000008067018,0.00003706421,0.001100921,0.000007879725,0.0008349913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05059487,"threshold_uncertainty_score":0.3670932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03875528882574571,"score_gpt":0.2898746225152597,"score_spread":0.251119333689514,"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."}}