{"id":"W2807006973","doi":"10.1145/3209281.3209385","title":"Spatial, temporal and semantic contextualization of citizen participation","year":2018,"lang":"en","type":"article","venue":"","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Contextualization; Computer science; Natural language processing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004089693,0.0003288553,0.0002470569,0.002264779,0.002010056,0.004513834,0.0005260622,0.000789445,0.00266595],"category_scores_gemma":[0.009639778,0.000211495,0.0005674082,0.00261833,0.00578585,0.005573737,0.003816988,0.0006787656,0.0001985716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00250512,"about_ca_system_score_gemma":0.002363658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009471037,"about_ca_topic_score_gemma":0.01025407,"domain_scores_codex":[0.9919152,0.005845257,0.000331383,0.0006259236,0.0007903572,0.0004918964],"domain_scores_gemma":[0.9939704,0.003000168,0.001209257,0.0006818891,0.0009039993,0.000234241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003298513,0.0001263236,0.0668752,0.001039663,0.0001334994,0.001431802,0.1542462,0.008464824,0.006441443,0.6400707,0.002598976,0.1182415],"study_design_scores_gemma":[0.00003498068,0.0002051913,0.1222496,0.0008955118,0.0002172542,0.001257148,0.3293075,0.01689856,0.004550381,0.3666309,0.15757,0.0001829751],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5819309,0.00330701,0.2080124,0.009098711,0.0002444055,0.000430018,0.001031773,0.0002128242,0.1957319],"genre_scores_gemma":[0.9912451,0.0002514868,0.007404601,0.0000818977,0.0000236126,0.00007212244,0.0001036153,0.00001460505,0.0008029363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009471037,"threshold_uncertainty_score":0.02162868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673003933126209,"score_gpt":0.2351888692857443,"score_spread":0.2184588299544822,"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."}}