{"id":"W3103142363","doi":"10.1177/0049085720957512","title":"Access to Non-farm Employment in Contemporary India: A Study of Bihar and Punjab","year":2020,"lang":"en","type":"article","venue":"Social Change","topic":"Agricultural Innovations and Practices","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"","keywords":"Caste; Livelihood; Agriculture; Diversification (marketing strategy); Agrarian society; Context (archaeology); Business; Economic growth; Socioeconomics; Work (physics); Agricultural economics; Geography; Economics; Political science; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0006007577,0.0004091987,0.0004605833,0.001615445,0.005862742,0.002712475,0.001352567,0.0008565468,0.003183019],"category_scores_gemma":[0.001146562,0.0008606046,0.000330945,0.003417677,0.002930895,0.001250529,0.002277935,0.001668067,0.000650899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002108798,"about_ca_system_score_gemma":0.002003341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.109348,"about_ca_topic_score_gemma":0.2411732,"domain_scores_codex":[0.9992539,0.0002039601,0.00003893553,0.00009818702,0.00008886156,0.0003160437],"domain_scores_gemma":[0.9989415,0.0002960943,0.0002379958,0.00006172037,0.0001023286,0.0003603403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001298678,0.00075347,0.554329,0.0002215586,0.00006886074,0.003621135,0.4236428,0.0000897354,0.002700424,0.001236935,0.0009457167,0.01226053],"study_design_scores_gemma":[0.000006492741,0.0001963888,0.6940674,0.00006710669,0.00002106165,0.001056207,0.3017224,0.00007403422,0.0001019675,0.00008123101,0.002573828,0.00003193974],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989429,0.00008003249,0.00001764026,0.0001328556,0.000002732988,0.000008350153,0.00004037321,0.000001439478,0.0007737288],"genre_scores_gemma":[0.9989202,0.0002268319,0.0000554148,0.0001828257,0.000003451976,0.00001962142,0.00006317763,0.000003173193,0.000525255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.109348,"threshold_uncertainty_score":0.217423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3204591129935006,"score_gpt":0.3578938125295932,"score_spread":0.03743469953609263,"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."}}