{"id":"W6922548737","doi":"10.13140/rg.2.2.12549.14569","title":"Investigating Rural Precarious Employment in Ontario","year":2017,"lang":"en","type":"article","venue":"","topic":"Rural development and sustainability","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Work (physics); Government (linguistics); Unemployment; Agency (philosophy)","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":[0.0005312942,0.0001501224,0.0002784687,0.0009723156,0.006260743,0.001803358,0.0009384883,0.0004405622,0.003184319],"category_scores_gemma":[0.002090973,0.0002208705,0.0002107191,0.003198127,0.00149327,0.0005944519,0.001607966,0.0005665686,0.0002677719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04110558,"about_ca_system_score_gemma":0.04440158,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9916328,"about_ca_topic_score_gemma":0.9985369,"domain_scores_codex":[0.9993675,0.00007584695,0.00001675119,0.00005612073,0.0001291495,0.0003545674],"domain_scores_gemma":[0.9980386,0.0003275195,0.0003348832,0.00007239519,0.000696671,0.0005298884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002384153,0.0001644146,0.8498141,0.0001858029,0.00005315251,0.001310789,0.1097451,0.0008555733,0.001252204,0.005940544,0.00427191,0.02616807],"study_design_scores_gemma":[0.000008309995,0.00004110054,0.8412231,0.00007170194,0.00001643509,0.00008549447,0.1407839,0.0004608711,0.0001797605,0.0004051818,0.01671108,0.00001306674],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887975,0.0001813316,0.0001216846,0.0005541474,0.000005650438,0.00004108623,0.0005801395,0.000003099003,0.00971535],"genre_scores_gemma":[0.9930016,0.0002551524,0.0001743071,0.00009690812,0.000004283203,0.0000296149,0.0002191031,0.000005065005,0.006214145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04110558,"threshold_uncertainty_score":0.2982433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186219347482425,"score_gpt":0.2373353813857348,"score_spread":0.2054731879109106,"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."}}