{"id":"W4414590743","doi":"10.1108/meq-12-2024-0587","title":"Unveiling two decades of environmental policy research trends: topic modeling-based machine learning insights","year":2025,"lang":"en","type":"article","venue":"Management of Environmental Quality An International Journal","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Ted Rogers Centre for Heart Research","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Scholarship; Profiling (computer programming); Field (mathematics); Policy analysis; Analytics; Climate change; Key (lock)","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01428843,0.0003667582,0.0008727667,0.01456247,0.001141693,0.007285866,0.0009106259,0.001626867,0.002482001],"category_scores_gemma":[0.03568079,0.0003899974,0.0009121803,0.02297885,0.002187325,0.01013605,0.002829888,0.00232694,0.0006093915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002993377,"about_ca_system_score_gemma":0.00431785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005847895,"about_ca_topic_score_gemma":0.008043066,"domain_scores_codex":[0.9943932,0.002203928,0.0006732178,0.001201891,0.00119377,0.0003338875],"domain_scores_gemma":[0.9372827,0.04510938,0.008774267,0.003195827,0.004358349,0.001279475],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004051716,0.0002424097,0.4292877,0.00506821,0.0005968648,0.0008898164,0.03084534,0.01274557,0.002909799,0.1062954,0.02293019,0.3877837],"study_design_scores_gemma":[0.00005921509,0.0002853218,0.4266813,0.005847141,0.0004412818,0.0008657166,0.05357834,0.06294402,0.00283602,0.2070273,0.2391928,0.0002415492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6797875,0.09543499,0.09011374,0.0767922,0.001430614,0.0003548193,0.01450851,0.0006099761,0.04096759],"genre_scores_gemma":[0.9569914,0.01513906,0.01898626,0.002114815,0.0009555599,0.000262055,0.003878527,0.0001333205,0.001538974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9857116,"threshold_uncertainty_score":0.07556534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06124018371179857,"score_gpt":0.3966194207805703,"score_spread":0.3353792370687717,"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."}}