{"id":"W4254534282","doi":"10.32920/ryerson.14638986","title":"The Segmentation of Academic Labour","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Segmentation; Corporatization; Market segmentation; Labour economics; Economics; Business; Public relations; Political science; Marketing; Market economy; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007059861,0.0003298321,0.0005476066,0.005893826,0.01207599,0.01012008,0.001820361,0.002012538,0.01224839],"category_scores_gemma":[0.02021801,0.0002964485,0.0005460732,0.01241135,0.01345854,0.005389251,0.007279503,0.001752418,0.001389656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05121268,"about_ca_system_score_gemma":0.07777932,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6905452,"about_ca_topic_score_gemma":0.7648617,"domain_scores_codex":[0.9833925,0.002495237,0.0005743831,0.001279126,0.006312137,0.005946603],"domain_scores_gemma":[0.9849246,0.003497496,0.002302936,0.0008703221,0.004642787,0.003761809],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002615842,0.00007110294,0.1026667,0.0009323532,0.00007199503,0.001150323,0.100416,0.001162586,0.002271171,0.5763037,0.04159473,0.1730978],"study_design_scores_gemma":[0.00004463538,0.00008806778,0.2609834,0.001790832,0.00003488731,0.0004646601,0.165468,0.0009320597,0.0006896574,0.1131445,0.4562205,0.0001387217],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.472473,0.01448181,0.006607177,0.08540769,0.0006765875,0.0002751199,0.001645026,0.0001125744,0.418321],"genre_scores_gemma":[0.9719686,0.005265922,0.001720837,0.006141158,0.0001975652,0.00006986818,0.0006771366,0.00005515645,0.01390379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9929401,"threshold_uncertainty_score":0.6225544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07579123349515268,"score_gpt":0.4970444962894741,"score_spread":0.4212532627943214,"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."}}