{"id":"W4221127616","doi":"10.1002/cjas.1669","title":"Acknowledgements – Remerciements","year":2022,"lang":"fr","type":"article","venue":"Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Citation; Library science; Computer science; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.009021481,0.0004174317,0.000528698,0.000941184,0.01150117,0.0008998486,0.002567576,0.0001591843,0.01411363],"category_scores_gemma":[0.001874522,0.0004586624,0.0002988231,0.004577662,0.01755811,0.002071285,0.00008456452,0.0007683143,0.000112667],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004830976,"about_ca_system_score_gemma":0.03767361,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05103981,"about_ca_topic_score_gemma":0.772993,"domain_scores_codex":[0.9934658,0.001609714,0.001174837,0.000683099,0.0008351806,0.002231356],"domain_scores_gemma":[0.9942868,0.0003301055,0.001207693,0.0002369686,0.0009892933,0.002949142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001365851,0.0006088967,0.0477056,0.0003155798,0.0002861398,0.003009788,0.4552188,0.003681737,0.0003810732,0.381388,0.08273417,0.02453364],"study_design_scores_gemma":[0.0005002528,0.01187584,0.003669603,0.0003387786,0.0001609012,0.001473684,0.1156195,0.0006380188,0.0003496649,0.04936687,0.815179,0.0008278749],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8357221,0.006560957,0.0001140765,0.01904809,0.01029056,0.0004340221,0.0004069132,0.00001395838,0.1274093],"genre_scores_gemma":[0.9666414,0.0001778867,0.002896426,0.0008984815,0.001517704,0.00002116366,0.000008702018,0.00001947727,0.02781876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7324449,"threshold_uncertainty_score":0.9997865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1704305830071935,"score_gpt":0.3890709972957153,"score_spread":0.2186404142885218,"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."}}