{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002824167,0.0003827667,0.0006740026,0.0005220765,0.0004484045,0.001184814,0.0005510305,0.0002321337,0.0003172091],"category_scores_gemma":[0.0004455796,0.0003034842,0.000605202,0.0008228982,0.00003074217,0.0005039055,0.0008053023,0.0003181194,0.000004604459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008832881,"about_ca_system_score_gemma":0.00007801634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002724628,"about_ca_topic_score_gemma":0.001386725,"domain_scores_codex":[0.9974802,0.00007362488,0.0008037582,0.0007923804,0.0004120018,0.0004380329],"domain_scores_gemma":[0.9979202,0.0002062395,0.0007443404,0.0007282367,0.0003726099,0.00002836989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005533634,0.0003750528,0.2099875,0.002518846,0.01050633,0.00001078584,0.00555446,0.5654853,0.01111708,0.008396096,0.05703596,0.1284592],"study_design_scores_gemma":[0.0009973857,0.00001997623,0.01954926,0.0005210975,0.008055749,5.794619e-7,0.006180282,0.9394028,0.001325688,0.0007671436,0.02184103,0.001339028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.929581,0.0005255187,0.06184612,0.001057133,0.001753664,0.001409434,0.000007827266,0.0002348581,0.003584392],"genre_scores_gemma":[0.9801908,0.00007658145,0.009446644,0.0008471245,0.004275942,0.000246339,0.001913095,0.00009461617,0.0029089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3739175,"threshold_uncertainty_score":0.9999417,"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."}}