{"id":"W3215723077","doi":"10.32920/ryerson.14657964.v1","title":"Gearing up for Gen Z: An Analysis of Employers’ Recruitment Marketing Targeting the New, Generation Z, Workforce","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Employer Branding and e-HRM","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Workforce; Business; Marketing; Generation y; Public relations; Political 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":[0.0023641,0.0001270246,0.0001504075,0.001762948,0.001501014,0.002357851,0.0002850447,0.000651114,0.003947446],"category_scores_gemma":[0.006292072,0.0001269748,0.0002172967,0.001939691,0.001189391,0.001720169,0.001210875,0.0005803257,0.0005061472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002141439,"about_ca_system_score_gemma":0.00171567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00665187,"about_ca_topic_score_gemma":0.01388151,"domain_scores_codex":[0.9989536,0.0005459494,0.00003783689,0.00006623876,0.0002240732,0.0001722798],"domain_scores_gemma":[0.9930675,0.005394617,0.0007919027,0.0001427608,0.0003833917,0.000219781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006265503,0.001153711,0.4389683,0.0005678728,0.00004874901,0.001481241,0.3701119,0.0006371129,0.007388473,0.03066223,0.006033638,0.1423203],"study_design_scores_gemma":[0.00002038573,0.0001968926,0.6773848,0.0001554772,0.00003501984,0.0002252333,0.2953917,0.002598786,0.001690501,0.002406501,0.01987163,0.0000229517],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885112,0.00009119017,0.0007281039,0.0004457736,0.000004597842,0.00004741905,0.00006871924,0.000009088132,0.01009392],"genre_scores_gemma":[0.994693,0.0001191473,0.0007272576,0.0001784823,0.000008669392,0.00004900559,0.0001155674,0.00001178252,0.004097004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00665187,"threshold_uncertainty_score":0.01553732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1295534551468955,"score_gpt":0.3135323707620456,"score_spread":0.1839789156151501,"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."}}