{"id":"W2770861095","doi":"10.1007/s11625-017-0509-2","title":"Designing cultural multilevel selection research for sustainability science","year":2017,"lang":"en","type":"article","venue":"Sustainability Science","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"University of California, Davis; National Institute of Food and Agriculture; National Socio-Environmental Synthesis Center; Arizona State University; John Templeton Foundation; U.S. Department of Agriculture; National Science Foundation","keywords":"Sustainability; Group selection; Sociology; Management science; Computer science; Data science; Ecology; Selection (genetic algorithm); Engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.04910883,0.0001541839,0.0001719242,0.0003605873,0.04356383,0.002103106,0.002611374,0.0001065901,0.00003285988],"category_scores_gemma":[0.1177785,0.0001453987,0.00007721553,0.0017705,0.03328574,0.006382709,0.0004694406,0.0003537994,0.000007409481],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.007287678,"about_ca_system_score_gemma":0.01824428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003900415,"about_ca_topic_score_gemma":0.0009780169,"domain_scores_codex":[0.9943733,0.0006438979,0.0003022026,0.00111712,0.001863817,0.001699645],"domain_scores_gemma":[0.9780873,0.0006327668,0.0001600658,0.0009029867,0.0198259,0.0003909601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001595186,0.000190333,0.03363554,0.00009367974,0.000002467962,0.000001476065,0.02425983,0.0003429669,0.007713499,0.9098607,0.0001267758,0.02361318],"study_design_scores_gemma":[0.0004259686,0.0002915221,0.1066723,0.00001762591,0.000008035975,0.000002214845,0.1172121,0.0071042,0.008402327,0.7532476,0.00621404,0.0004021385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654916,0.00001891976,0.01203622,0.01030963,0.0004623178,0.003219736,0.000004232241,0.0001479759,0.00830938],"genre_scores_gemma":[0.9943027,0.000003710834,0.002348212,0.00002937197,0.0002528515,0.00028699,9.36199e-7,0.00000709563,0.002768119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1566132,"threshold_uncertainty_score":0.9989328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1079415904315881,"score_gpt":0.4950635250755664,"score_spread":0.3871219346439784,"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."}}