{"id":"W2947414555","doi":"10.1016/j.giq.2019.05.002","title":"From citizens to government policy-makers: Social media data analysis","year":2019,"lang":"en","type":"article","venue":"Government Information Quarterly","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":123,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Government (linguistics); Social media; Order (exchange); Semantic analysis (machine learning); Computer science; Data science; Public relations; World Wide Web; Business; Political science; Information retrieval","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00095278,0.0002295851,0.0003376594,0.00009024033,0.0003595306,0.0006910712,0.001299861,0.0001574486,0.003354575],"category_scores_gemma":[0.0001654263,0.0002350269,0.0001459104,0.001077695,0.00007027743,0.003536297,0.000213835,0.0001349408,0.002132263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442105,"about_ca_system_score_gemma":0.000167722,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007252693,"about_ca_topic_score_gemma":0.007527933,"domain_scores_codex":[0.9939592,0.0001229871,0.000606882,0.0003430864,0.004426761,0.0005410395],"domain_scores_gemma":[0.9981105,0.0003072779,0.0004417169,0.0007876838,0.0000665774,0.0002862792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002776088,0.0002067971,0.02567736,0.00004365879,0.001642506,0.000002139689,0.6354596,0.00005432774,0.00009994075,0.107647,0.09562432,0.1332647],"study_design_scores_gemma":[0.0007764705,0.00009520491,0.02961271,0.00001307226,0.0002180693,9.552743e-8,0.2868126,0.0006765777,0.00002001611,0.0007283348,0.6806145,0.0004323716],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6440844,0.00001698285,0.00185944,0.02834784,0.001344542,0.001000822,0.009235423,0.0002304925,0.3138801],"genre_scores_gemma":[0.9911132,0.00001227855,0.0005443834,0.004701278,0.001249907,0.00002976247,0.0009709575,0.00001230588,0.001365894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5849902,"threshold_uncertainty_score":0.9993581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189096325792784,"score_gpt":0.2810193508635658,"score_spread":0.2621097182842874,"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."}}