{"id":"W1862120493","doi":"","title":"Social Networks and the Targeting of Illegal Electoral Strategies","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Social Capital and Networks","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coercion (linguistics); Interpersonal ties; Intimidation; Politics; Social network (sociolinguistics); Political science; Democracy; Affect (linguistics); Public relations; Political economy; Social media; Business; Internet privacy; Public economics; Economics; Social psychology; Sociology; Law; Psychology; Computer science","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":[0.001015257,0.0001532818,0.0001369901,0.0014721,0.001289153,0.001393747,0.0002758913,0.0004203227,0.006253648],"category_scores_gemma":[0.008462947,0.0001148937,0.0001039516,0.0008888033,0.001125147,0.001141328,0.001432181,0.000434098,0.0004040657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445194,"about_ca_system_score_gemma":0.0003526667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798052,"about_ca_topic_score_gemma":0.004465138,"domain_scores_codex":[0.9988409,0.0006837845,0.00004314946,0.0001214998,0.0001553464,0.0001554317],"domain_scores_gemma":[0.9937754,0.002207476,0.002870796,0.0002559698,0.0003427411,0.0005476376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001468298,0.0002065316,0.8860768,0.0001415267,0.00009714512,0.000334494,0.01688413,0.0005212723,0.0008495184,0.01719752,0.001564145,0.0759801],"study_design_scores_gemma":[0.00002413713,0.0002004026,0.9219846,0.0003331896,0.00007897244,0.0008919794,0.04078519,0.002908538,0.0007880704,0.01330252,0.01866954,0.0000328876],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806122,0.0003307061,0.0007691574,0.0008107823,0.00001468007,0.00001619219,0.00007376383,0.000005835191,0.01736656],"genre_scores_gemma":[0.9992742,0.00009221376,0.0001046753,0.00002221981,0.000004972085,0.000004817134,0.00001540072,0.000001272066,0.0004801296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006253648,"threshold_uncertainty_score":0.02092057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006988237051995329,"score_gpt":0.2567286060387109,"score_spread":0.2497403689867156,"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."}}