{"id":"W3125824511","doi":"10.5509/2012853483","title":"Cellular Mobile in India: Competition and Policy","year":2012,"lang":"en","type":"article","venue":"Pacific Affairs","topic":"ICT Impact and Policies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Competition (biology); Business; Political science; Economic geography; Economics; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007163101,0.00008334807,0.0000902418,0.000141856,0.0000217789,0.00001428016,0.00003062165,0.00005449051,0.00004985314],"category_scores_gemma":[0.000005094621,0.00008341879,0.00001558644,0.000146577,0.00002828098,0.0001298957,0.00001123273,0.00008262235,0.00008992307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004350758,"about_ca_system_score_gemma":0.000005300457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002579366,"about_ca_topic_score_gemma":0.000001646381,"domain_scores_codex":[0.9995574,0.00001145199,0.00008322784,0.00002676022,0.00005049386,0.0002706833],"domain_scores_gemma":[0.9998057,0.00002011933,0.000008453307,0.00008170149,0.000003485105,0.00008049269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000039456,0.0004379642,0.1628398,0.001047626,0.0001503584,0.00001766742,0.4823612,0.004148558,0.145109,0.1675455,0.00921364,0.02708925],"study_design_scores_gemma":[0.00209701,0.0002193574,0.1525283,0.0002006242,0.00004575915,0.00009276604,0.6097974,0.003058661,0.1132548,0.001531093,0.115291,0.001883126],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5406563,0.001261097,0.0001046958,0.00001731831,0.0001991643,0.0001037345,0.00001162907,0.000123828,0.4575222],"genre_scores_gemma":[0.9994317,0.0001371937,0.00002687548,0.000003624656,0.0002244047,0.00001501855,0.000009931297,0.00001608309,0.0001351326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4587754,"threshold_uncertainty_score":0.3401719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00576511818099308,"score_gpt":0.2117581862443626,"score_spread":0.2059930680633696,"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."}}