{"id":"W2426721675","doi":"","title":"Not Good or Bad But Different: Free Markets, Subjective Preferences, and Labels for Genetically Modified Foods","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Value (mathematics); Purchasing; Assertion; Business; Competition (biology); Government (linguistics); Labelling; Economics; Meaning (existential); Autonomy; Genetically modified organism; Price premium; Marketing; Law and economics; Microeconomics; Willingness to pay; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.001093716,0.0002798458,0.0003091333,0.00003406551,0.000393115,0.0001474082,0.000778297,0.0002082681,0.0002797976],"category_scores_gemma":[0.0005074461,0.00008454137,0.0001174877,0.0001604785,0.0001883928,0.0001131673,0.0002439512,0.0006709025,0.00001092285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002483808,"about_ca_system_score_gemma":0.0004006083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005886495,"about_ca_topic_score_gemma":0.004025187,"domain_scores_codex":[0.9958663,0.0002343131,0.0003568141,0.000505419,0.0004968709,0.002540255],"domain_scores_gemma":[0.9983574,0.0008729401,0.000111433,0.0001343533,0.0002115176,0.0003123214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001179584,0.0001486446,0.001755812,0.000008103686,0.000153826,0.000002502817,0.00002292076,2.683479e-7,0.4965108,0.04425446,0.00005140068,0.4559117],"study_design_scores_gemma":[0.003861228,0.007932224,0.3045538,0.00008050264,0.00009547894,0.0003306314,0.0003995979,0.00007831875,0.04585,0.6347346,0.00129474,0.0007888965],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913123,0.0006873636,0.002937882,0.003988668,0.00007696857,0.0005182716,0.00011302,0.00003835694,0.0003271408],"genre_scores_gemma":[0.9909077,0.004732687,0.0002570775,0.00008171863,0.0003559263,0.00004903368,0.000005117435,0.00000596227,0.003604834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5904801,"threshold_uncertainty_score":0.3447497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672193241485341,"score_gpt":0.2437285439485165,"score_spread":0.2170066115336631,"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."}}