{"id":"W2795629208","doi":"10.1111/cjag.12169","title":"Unraveling determinants of inferred and stated attribute nonattendance: Effects on farmers’ willingness to accept to join agri‐environmental schemes","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria","keywords":"Bivariate analysis; Context (archaeology); Logit; Willingness to accept; Incentive; Willingness to pay; Multivariate probit model; Attendance; Ordered probit; Econometrics; Economics; Microeconomics; Geography; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.009191948,0.0002793771,0.0004938824,0.0004032378,0.0003905396,0.002720001,0.0006984739,0.001229751,0.007122514],"category_scores_gemma":[0.03217837,0.000243209,0.0008068829,0.0005982501,0.001045206,0.001640144,0.001089182,0.00227477,0.0004674323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028394,"about_ca_system_score_gemma":0.0007744307,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007622353,"about_ca_topic_score_gemma":0.01055797,"domain_scores_codex":[0.9945713,0.003821027,0.0001936069,0.0005155898,0.0004773272,0.0004211453],"domain_scores_gemma":[0.8629914,0.1123525,0.01441967,0.004928552,0.002649995,0.002657744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000827923,0.001617081,0.9731851,0.00008367341,0.0003177204,0.000156457,0.00206824,0.00466653,0.002111336,0.002540707,0.000190149,0.01223513],"study_design_scores_gemma":[0.00002519014,0.0005469432,0.9797386,0.00003723643,0.0001319844,0.0000281453,0.003109682,0.01195223,0.0006010438,0.003098429,0.0006990288,0.00003151498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981441,0.00003698907,0.0006916932,0.000137468,0.000002296415,0.00001083546,0.00004242322,0.000003634817,0.0009305104],"genre_scores_gemma":[0.9992673,0.00001832435,0.000334865,0.00001715254,0.00000228054,0.000009341463,0.00004114711,0.000002033672,0.0003075365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9923776,"threshold_uncertainty_score":0.04861224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04901628843782947,"score_gpt":0.1866578970098975,"score_spread":0.1376416085720681,"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."}}