{"id":"W3201316946","doi":"10.1017/iop.2021.79","title":"“I” feel(s) left out: The importance of information and communication technology in personnel selection research","year":2021,"lang":"en","type":"article","venue":"Industrial and Organizational Psychology","topic":"Employer Branding and e-HRM","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Selection (genetic algorithm); Psychology; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006061434,0.00005653656,0.00009609679,0.0002902834,0.0002093962,0.00006538342,0.0001053088,0.0001643362,0.000119494],"category_scores_gemma":[0.0006296306,0.00004758312,0.000007424259,0.0009363419,0.0001648576,0.0003723829,0.0001089194,0.0002842257,0.00001447653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001335687,"about_ca_system_score_gemma":0.00003663672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008204876,"about_ca_topic_score_gemma":0.0003701336,"domain_scores_codex":[0.9993847,0.0000395396,0.0002270189,0.0001187321,0.0001149744,0.0001150574],"domain_scores_gemma":[0.9993766,0.0000800976,0.0001013965,0.0001140177,0.0003224857,0.000005429421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003850313,0.00003630881,0.9219669,0.00001403707,0.00001017124,6.331655e-7,0.0003800853,0.000007959394,0.0005314275,0.07133687,0.003165668,0.002511462],"study_design_scores_gemma":[0.0058093,0.00007781221,0.6899751,0.0001540837,0.00004612196,0.0001181559,0.009337648,0.0007763548,0.0009175279,0.1931558,0.09922848,0.0004036008],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734438,0.0001834026,0.00008232886,0.02388232,0.0001467315,0.0001156807,0.000002299775,0.00002230604,0.002121106],"genre_scores_gemma":[0.9987946,0.0000685581,0.00005923143,0.0007541468,0.0002002339,0.000003864554,0.0000595832,0.000005770375,0.00005408225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2319918,"threshold_uncertainty_score":0.1940383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07626210580591998,"score_gpt":0.3214349949540172,"score_spread":0.2451728891480972,"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."}}