{"id":"W4247837154","doi":"10.36227/techrxiv.12061392","title":"Developing better Civic Services through Crowdsourcing: The Twitter Case Study","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Crowdsourcing; Government (linguistics); Civic engagement; Public relations; Conversation; Internet privacy; Democracy; Social media; Open innovation; Business; Feeling; Political science; World Wide Web; Computer science; Marketing; Sociology; Psychology; Politics","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004391634,0.000318469,0.0002821171,0.00006511225,0.0004032112,0.001543869,0.002293852,0.0001192876,0.00005008209],"category_scores_gemma":[0.0000146143,0.0001966492,0.0000908952,0.0003706532,0.00002667036,0.0009894259,0.00713448,0.0006546336,0.0002882856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000480519,"about_ca_system_score_gemma":0.0001558877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008611595,"about_ca_topic_score_gemma":0.00008418641,"domain_scores_codex":[0.9979905,0.0001091393,0.0005502466,0.0005743756,0.0004707949,0.0003048752],"domain_scores_gemma":[0.9982309,0.0001217757,0.0002715314,0.001181675,0.0001342019,0.00005995228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002266215,0.0001982878,0.01290732,0.001439643,0.0008329345,0.008394768,0.897152,0.001115059,0.00001470145,0.02956569,0.0145821,0.03377483],"study_design_scores_gemma":[0.003277956,0.0005420747,0.009113837,0.0009921485,0.000371168,0.007911383,0.2100728,0.5211987,0.00202352,0.04393483,0.1944108,0.006150691],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2066017,0.00003045509,0.7731099,0.01442509,0.0007894522,0.0006390616,0.000005560655,0.0003434883,0.004055284],"genre_scores_gemma":[0.8862761,0.00000345991,0.07972854,0.03352772,0.0002685082,0.00004723661,0.00002278234,0.0000146843,0.0001109899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6933814,"threshold_uncertainty_score":0.9994926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0990680910389135,"score_gpt":0.318099549592651,"score_spread":0.2190314585537375,"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."}}