{"id":"W6983031250","doi":"","title":"Learning from Workforce Studies that Cut Across Social\\nServices","year":2020,"lang":"en","type":"article","venue":"Insecta mundi","topic":"Engineering and Material Science Research","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workforce; Psychological intervention; Welfare; Job analysis; Intervention (counseling); Set (abstract data type); Job attitude; Social work","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1561379,0.001866058,0.003735113,0.01907171,0.003939789,0.01177596,0.003617242,0.005890395,0.01388074],"category_scores_gemma":[0.4724632,0.001506045,0.005277665,0.01428166,0.007323399,0.02448383,0.0107958,0.007919024,0.002275613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004947538,"about_ca_system_score_gemma":0.01102631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01031021,"about_ca_topic_score_gemma":0.02427094,"domain_scores_codex":[0.869694,0.07026175,0.03138367,0.01035422,0.01594463,0.002361846],"domain_scores_gemma":[0.3966902,0.5053419,0.0297831,0.02936279,0.03553529,0.003286639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005576409,0.0002417234,0.03336794,0.2227599,0.01111482,0.001064442,0.04830829,0.0006157272,0.00179308,0.05501802,0.1750402,0.4501182],"study_design_scores_gemma":[0.0003959272,0.0005078189,0.02994567,0.3231148,0.006840001,0.001268093,0.02901354,0.0003593724,0.001722081,0.0980006,0.5085919,0.0002401841],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.02230919,0.5744959,0.06508776,0.272016,0.02564422,0.002799338,0.00797979,0.0004220282,0.02924574],"genre_scores_gemma":[0.2264011,0.2308105,0.1330608,0.3687166,0.01938372,0.0113254,0.004882446,0.0009393097,0.004480021],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1561379,"threshold_uncertainty_score":0.8257462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.134187526647301,"score_gpt":0.3619164698695557,"score_spread":0.2277289432222547,"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."}}