{"id":"W2039417394","doi":"10.1016/j.tej.2007.02.005","title":"How Effective Are M&amp;As in Distribution? Evaluating the Government's Policy of Using Mergers and Amalgamations to Drive Efficiencies into Ontario's LDCs","year":2007,"lang":"en","type":"article","venue":"The Electricity Journal","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Restructuring; Incentive; Scope (computer science); Government (linguistics); Distribution (mathematics); Industrial organization; Welfare; Economics; Business; Public economics; Microeconomics; Finance; Market economy; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003056764,0.00008892949,0.0001360556,0.00007024562,0.001313462,0.0001467948,0.0002556993,0.00004562466,0.000008360504],"category_scores_gemma":[0.00364329,0.00006151599,0.00005438708,0.001096544,0.0002473941,0.0001617348,0.00003630512,0.0003531518,8.417352e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002704696,"about_ca_system_score_gemma":0.0007549793,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4665592,"about_ca_topic_score_gemma":0.7575458,"domain_scores_codex":[0.9982903,0.0003130051,0.0001976135,0.0001043157,0.0006904518,0.0004043302],"domain_scores_gemma":[0.9986565,0.0006775927,0.0003124865,0.0001000138,0.0001200013,0.000133434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0005991578,0.0002207962,0.1217599,0.00002913563,0.0002290774,0.00002355995,0.6330253,0.009355683,0.03131705,0.1213264,0.004376302,0.07773756],"study_design_scores_gemma":[0.0008088606,0.0003594551,0.9402021,0.000185337,0.0001012393,0.00007134533,0.022915,0.0008417377,0.00383323,0.01458583,0.01569923,0.0003967097],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824628,0.0001288453,0.002888489,0.0137013,0.00005253317,0.0003118457,0.00001579134,0.000003545044,0.0004348374],"genre_scores_gemma":[0.9987112,0.00002683048,0.00008172596,0.0004923242,0.0002162505,0.000003513539,2.534111e-7,0.000004134813,0.0004637882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8184421,"threshold_uncertainty_score":0.9999867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02588968658275412,"score_gpt":0.3485487290078418,"score_spread":0.3226590424250877,"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."}}