{"id":"W2572951772","doi":"10.1145/3040230.3040239","title":"Search (Non-)Neutrality and Impact on Innovation","year":2017,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Neutrality; Business; Political science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01529553,0.0004418309,0.001198737,0.002547304,0.0006058066,0.004263637,0.0007516752,0.001340157,0.03814039],"category_scores_gemma":[0.1155509,0.0001640618,0.001385265,0.00389101,0.001607966,0.003173392,0.001419108,0.001163688,0.002954038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002007341,"about_ca_system_score_gemma":0.002774491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002912724,"about_ca_topic_score_gemma":0.003387035,"domain_scores_codex":[0.9889165,0.006167294,0.0007647874,0.0006067731,0.002649455,0.0008952703],"domain_scores_gemma":[0.8296993,0.1406045,0.01196449,0.004694744,0.009455052,0.003581964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.013559,0.0009379695,0.1467825,0.01332319,0.005821364,0.0003994563,0.0007715407,0.01221557,0.002565239,0.1112433,0.04525562,0.6471252],"study_design_scores_gemma":[0.002948798,0.01212939,0.4324887,0.006467922,0.0145161,0.001911397,0.001982881,0.02786577,0.008819059,0.3809095,0.1096368,0.0003237848],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4511942,0.1489714,0.01409676,0.02862609,0.001257365,0.0007590621,0.01037499,0.000483117,0.344237],"genre_scores_gemma":[0.9729023,0.01495214,0.0009578456,0.0007015659,0.0004019754,0.0001083033,0.0008577945,0.00004723754,0.00907078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03814039,"threshold_uncertainty_score":0.1275923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1423192492154084,"score_gpt":0.4026222062088647,"score_spread":0.2603029569934563,"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."}}