{"id":"W2124637600","doi":"10.1002/mcda.352","title":"<i>e</i>‐democracy and participatory decision processes: lessons from <i>e</i>‐negotiation experiments","year":2003,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta","keywords":"Negotiation; E-democracy; Democracy; Software deployment; Citizen journalism; e-participation; Participatory democracy; Participatory design; Public relations; Deliberative democracy; Voting; Knowledge management; E-Government; Sociology; State (computer science); Government (linguistics); Political science; Public administration; Management science; Information and Communications Technology; Computer science; Engineering; Politics; Social science; Operations management; Law","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001720868,0.0003058134,0.0007941575,0.0008696507,0.0003162324,0.0005571932,0.0006831491,0.0001535863,0.0001658125],"category_scores_gemma":[0.001512909,0.0002464076,0.0003501043,0.001666754,0.0000373386,0.001695287,0.0001385917,0.0002015159,0.00002991974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001138132,"about_ca_system_score_gemma":0.0001565088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000484904,"about_ca_topic_score_gemma":0.00006346522,"domain_scores_codex":[0.995986,0.000454464,0.00157526,0.0005773017,0.001080385,0.000326619],"domain_scores_gemma":[0.9960659,0.000851866,0.001232026,0.0006731414,0.0008025797,0.000374504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00121696,0.007936836,0.3144134,0.0002373199,0.008676062,0.0007160201,0.03499018,0.01255566,0.2165071,0.002631952,0.01700714,0.3831114],"study_design_scores_gemma":[0.01783197,0.0006643211,0.2773018,0.001193914,0.003488116,0.0002272426,0.002338025,0.6115609,0.05217527,0.01106146,0.01982068,0.002336286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3679083,0.001552122,0.6295012,0.0001570177,0.0007332034,0.0001053777,0.000007231536,0.00002083403,0.00001476473],"genre_scores_gemma":[0.8241156,0.0004550923,0.1750905,0.0002036493,0.00007258064,0.000005673703,0.000004413821,0.00001538903,0.00003711581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5990052,"threshold_uncertainty_score":0.9999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07943731263466125,"score_gpt":0.3762557129711533,"score_spread":0.2968184003364921,"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."}}