{"id":"W1987538888","doi":"10.1111/j.1744-6570.2005.00516.x","title":"DEVELOPING A NOMOLOGICAL NETWORK FOR INTERVIEW STRUCTURE: ANTECEDENTS AND CONSEQUENCES OF THE STRUCTURED SELECTION INTERVIEW","year":2005,"lang":"en","type":"article","venue":"Personnel Psychology","topic":"Employer Branding and e-HRM","field":"Business, Management and Accounting","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary","funders":"","keywords":"Interview; Psychology; Sophistication; Semi-structured interview; Social psychology; Consistency (knowledge bases); Sample (material); Perception; Selection (genetic algorithm); Applied psychology; Qualitative research; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1434316,0.0003964529,0.0004091482,0.006287673,0.003795714,0.002672072,0.001551471,0.0009501196,0.001666544],"category_scores_gemma":[0.3561786,0.0005071108,0.0005903792,0.003745757,0.004475273,0.007170171,0.004782609,0.00134324,0.0004674205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004588337,"about_ca_system_score_gemma":0.004942251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003619038,"about_ca_topic_score_gemma":0.008859142,"domain_scores_codex":[0.874234,0.1008469,0.006837496,0.003408496,0.01351145,0.001161766],"domain_scores_gemma":[0.5038901,0.3641807,0.05073832,0.02181232,0.05647783,0.002900742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004940539,0.0003119462,0.555011,0.001940995,0.0001554966,0.0005330617,0.1151915,0.003111397,0.002868912,0.07911918,0.003365666,0.2378968],"study_design_scores_gemma":[0.0002403354,0.001443445,0.4704349,0.002793663,0.0003316009,0.002782902,0.1821042,0.09447515,0.007772454,0.1787311,0.05845309,0.000437183],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4768276,0.001037857,0.4706285,0.003029188,0.0002719358,0.005901053,0.0005225773,0.000288744,0.04149255],"genre_scores_gemma":[0.8344116,0.0003320608,0.1584467,0.0002539566,0.00006835468,0.004805387,0.000415664,0.00004328886,0.001222923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1434316,"threshold_uncertainty_score":0.7585478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08005558209010136,"score_gpt":0.3195970936278164,"score_spread":0.239541511537715,"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."}}