{"id":"W2896552281","doi":"10.3390/publications6040041","title":"Planning for Academic Publishing after Retirement","year":2018,"lang":"en","type":"article","venue":"Publications","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Anticipation (artificial intelligence); Publishing; Publication; Research Assessment Exercise; Public relations; Mandatory retirement; Political science; Labour economics; Management; Sociology; Business; Economics; Law; Higher education; Computer 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":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.04772751,0.0006554979,0.0009806611,0.004079333,0.0106957,0.02920915,0.004651343,0.01373754,0.09169914],"category_scores_gemma":[0.1466901,0.0009741394,0.001697171,0.004278342,0.003172202,0.02260896,0.01133866,0.01103807,0.05292957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007041622,"about_ca_system_score_gemma":0.05795613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004480402,"about_ca_topic_score_gemma":0.01015283,"domain_scores_codex":[0.9743479,0.008520331,0.001941267,0.001898731,0.006704083,0.006587742],"domain_scores_gemma":[0.7675557,0.03765384,0.01244264,0.009020267,0.03288196,0.1404456],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002060489,0.0004253786,0.005124093,0.0006540898,0.0000435438,0.00148715,0.004640634,0.0004110491,0.0006644391,0.03733018,0.7547109,0.1943025],"study_design_scores_gemma":[0.0000934316,0.0001877009,0.004184671,0.0004779341,0.00002224501,0.0003609542,0.008013165,0.0001874705,0.0003774526,0.02080626,0.9652219,0.00006679038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03363344,0.01699662,0.01185973,0.672228,0.03463573,0.00136476,0.0008233858,0.00220167,0.2262567],"genre_scores_gemma":[0.2541439,0.02252435,0.06063643,0.1548989,0.03506995,0.003985762,0.002808224,0.001408912,0.4645235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9707909,"threshold_uncertainty_score":0.3067642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8246445626788835,"score_gpt":0.6557962135259534,"score_spread":0.1688483491529301,"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."}}