{"id":"W4295871799","doi":"10.53379/cjcd.2022.338","title":"Artificial Intelligence and Résumé Critique Experiences","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Career Development","topic":"Human Resource and Talent Management","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Salient; Leverage (statistics); Seekers; Coaching; Psychology; Field (mathematics); Computer science; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008592717,0.000535864,0.0006316,0.001436386,0.002782503,0.00768163,0.001365297,0.002269753,0.005754989],"category_scores_gemma":[0.04958915,0.0002736656,0.0004776148,0.0006798125,0.003352932,0.002790574,0.004026792,0.003158818,0.001019014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00156314,"about_ca_system_score_gemma":0.0009963356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009608361,"about_ca_topic_score_gemma":0.001879934,"domain_scores_codex":[0.98983,0.006642333,0.0004212573,0.0006124301,0.001850474,0.0006435704],"domain_scores_gemma":[0.9672792,0.02374422,0.002193733,0.001831599,0.001842096,0.003109122],"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":[0.0003412112,0.00131424,0.01501242,0.0004822019,0.00002995486,0.001638904,0.9168682,0.0005656155,0.00678451,0.003503923,0.004243525,0.04921534],"study_design_scores_gemma":[0.0001834363,0.00321688,0.06379262,0.0008022357,0.00006011837,0.002688263,0.7417381,0.003537328,0.00805336,0.005967275,0.1696165,0.0003438614],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801948,0.0003620719,0.0018057,0.001061347,0.00008780744,0.00008610105,0.0000403026,0.0001218961,0.01624005],"genre_scores_gemma":[0.9875526,0.0002188116,0.00145777,0.0003602678,0.00003362917,0.00006132379,0.00005346726,0.00005708706,0.01020491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008592717,"threshold_uncertainty_score":0.04544312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04469214767577176,"score_gpt":0.2240274550383967,"score_spread":0.179335307362625,"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."}}