{"id":"W2999822949","doi":"10.5430/ijhe.v9n2p184","title":"Talent Management in Academia – The Indian Business School Scenario","year":2020,"lang":"en","type":"article","venue":"International Journal of Higher Education","topic":"Human Resource and Talent Management","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Talent management; Human resource management; Knowledge management; Business; Talent development; Marketing; Human resources; Conceptual framework; Management; Public relations; Sociology; Computer science; Political science; Pedagogy; Economics; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002909785,0.0003417084,0.0002480496,0.002392031,0.009860042,0.01320987,0.00196459,0.002733768,0.003998031],"category_scores_gemma":[0.002450489,0.0002795215,0.0003586075,0.004577493,0.00564946,0.002964742,0.007092052,0.002644412,0.0007542066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01211169,"about_ca_system_score_gemma":0.01405011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05128902,"about_ca_topic_score_gemma":0.06093458,"domain_scores_codex":[0.9953418,0.001351129,0.0001296735,0.0002918157,0.0008025357,0.002083003],"domain_scores_gemma":[0.9941378,0.0009896326,0.0005681901,0.0001986428,0.0007259909,0.00337986],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002370674,0.00108856,0.07046221,0.0003910435,0.00003860657,0.01156701,0.05438264,0.009978196,0.003147433,0.7643803,0.01363949,0.07068738],"study_design_scores_gemma":[0.00009817942,0.0007453293,0.1013075,0.0006404142,0.00005795575,0.005279464,0.4113067,0.01908542,0.003641054,0.1951296,0.2622725,0.0004358229],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7448831,0.001408012,0.002700419,0.05457279,0.0001077355,0.00009657702,0.0001629912,0.0001515159,0.1959168],"genre_scores_gemma":[0.9943527,0.0004923254,0.0005717134,0.0006692248,0.00003483302,0.00001441207,0.00003008172,0.000006137656,0.003828644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9970902,"threshold_uncertainty_score":0.101981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834107073652605,"score_gpt":0.2742583065650605,"score_spread":0.2559172358285345,"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."}}