{"id":"W6966554832","doi":"10.3886/e129241","title":"Data and code for: Network Externality and Subsidy Structure in Two-Sided Markets: Evidence from Electric Vehicle Incentives","year":2021,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Subsidy; Counterfactual thinking; Electric vehicle; Externality; Liberian dollar; Incentive; Network effect; Relation (database)","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00349943,0.001037167,0.001497115,0.0003253205,0.0002952481,0.001266668,0.006474748,0.000509617,0.0002188389],"category_scores_gemma":[0.006405377,0.001098522,0.00005238452,0.001294977,0.0003733608,0.003795931,0.01186993,0.001550937,0.00002449617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002527813,"about_ca_system_score_gemma":0.0003836456,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006273219,"about_ca_topic_score_gemma":0.05922061,"domain_scores_codex":[0.9913822,0.0008781128,0.001189723,0.00436446,0.0009292205,0.001256257],"domain_scores_gemma":[0.9851086,0.003387089,0.001081909,0.009910624,0.0001572819,0.0003545062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007147315,0.00006760814,0.02347698,0.0003484585,0.0002571105,0.000175162,0.00003707039,0.000004016294,0.003044066,0.000003061957,0.9705448,0.001326981],"study_design_scores_gemma":[0.002682671,0.0000480394,0.0745467,0.004185724,0.0009361893,0.00008000459,0.00006578238,0.007546402,0.0001806389,0.001152366,0.9070312,0.001544299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1590838,0.04371348,0.00003094312,0.00007353458,0.0003187442,0.0009858833,0.7957285,0.00006435664,7.587724e-7],"genre_scores_gemma":[0.00893534,0.01436111,0.002099282,0.0002466568,0.0009006831,0.00003753308,0.973246,0.0001659605,0.000007378774],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1775175,"threshold_uncertainty_score":0.9997701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07928435655053769,"score_gpt":0.3590764797310022,"score_spread":0.2797921231804645,"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."}}