{"id":"W3036488616","doi":"10.1016/j.ijdrr.2020.101708","title":"Analysing the socioeconomic and motivational factors affecting the willingness to pay for climate change adaptation in Malaysia","year":2020,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Socioeconomic status; Contingent valuation; Willingness to pay; Agriculture; Climate change; Adaptation (eye); Environmental resource management; Business; Socioeconomics; Climate change adaptation; Public economics; Natural resource economics; Environmental economics; Economics; Geography; Environmental health; Psychology; Population","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":[0.001050865,0.0002163546,0.0001834817,0.0004645233,0.0004834509,0.001036805,0.0002992652,0.0005460068,0.003502246],"category_scores_gemma":[0.003458681,0.0002170344,0.0005791252,0.0004989775,0.0003946063,0.0004230233,0.0005758541,0.00127151,0.0002852255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419705,"about_ca_system_score_gemma":0.001603618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02922938,"about_ca_topic_score_gemma":0.04683626,"domain_scores_codex":[0.9994628,0.0001906503,0.00003331493,0.00003595133,0.00005503131,0.0002221839],"domain_scores_gemma":[0.9969013,0.0009762963,0.0009164045,0.00006538834,0.0002310685,0.0009096381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001021943,0.0003578761,0.9934152,0.00001650395,0.00006915961,0.0001345224,0.001469581,0.0004857059,0.0002223268,0.0002945388,0.0001179316,0.003314511],"study_design_scores_gemma":[0.000004108734,0.0001242195,0.9919498,0.00001454929,0.00002776808,0.00004768013,0.005980365,0.001420566,0.00007848628,0.0001320016,0.0002106715,0.000009765145],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996098,0.00001349351,0.00001857204,0.00007166112,0.000001133961,0.000002625487,0.00001811446,2.615163e-7,0.0002642504],"genre_scores_gemma":[0.9997296,0.00001703151,0.00002868501,0.000009179525,0.00000104932,0.000002488702,0.00001720951,2.461497e-7,0.0001945236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02922938,"threshold_uncertainty_score":0.05811846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1090036444871787,"score_gpt":0.2601506810266933,"score_spread":0.1511470365395146,"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."}}