{"id":"W7035961604","doi":"","title":"AI Operationalisation Chasm: Evidence from Canadian Public Administration","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Applications of artificial intelligence; Public sector; Qualitative research; Public policy","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":[0.04353827,0.0004204792,0.000964231,0.009638839,0.02165511,0.01387134,0.003871601,0.001921765,0.00494364],"category_scores_gemma":[0.1451741,0.001239959,0.0005268144,0.02051667,0.01676258,0.006024684,0.009388021,0.004493642,0.0004057507],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1722844,"about_ca_system_score_gemma":0.2862058,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9843284,"about_ca_topic_score_gemma":0.9880252,"domain_scores_codex":[0.9537152,0.008887682,0.002522328,0.002527166,0.02472655,0.007620979],"domain_scores_gemma":[0.7478852,0.104256,0.02586382,0.009526913,0.09999397,0.01247409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003242615,0.0002992295,0.1749124,0.001776348,0.0001133066,0.0006706432,0.6894845,0.000320544,0.0004490505,0.02123659,0.01007307,0.10034],"study_design_scores_gemma":[0.00003404379,0.00009261248,0.3063996,0.002399046,0.00007821619,0.0001102066,0.6307918,0.0005067769,0.0003119301,0.001525402,0.05764019,0.0001101948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241335,0.004957018,0.00084394,0.01379388,0.00008207616,0.0003589723,0.0006067043,0.00004074767,0.05518316],"genre_scores_gemma":[0.9938275,0.003083748,0.0004246224,0.0008198419,0.00001055475,0.0000829307,0.0001622327,0.00002599153,0.001562632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1722844,"threshold_uncertainty_score":0.9600328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2239963363784273,"score_gpt":0.4672868288018354,"score_spread":0.2432904924234081,"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."}}