{"id":"W2589272974","doi":"10.7577/ta.1956","title":"Your Comments Here: Contextualizing Technologies, Seeking Records and Supporting Transparency for Citizen Engagement","year":2017,"lang":"en","type":"article","venue":"Tidsskriftet Arkiv","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transparency (behavior); Public engagement; Public relations; Citizen science; Government (linguistics); Political science; Public participation; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002067455,0.0001725875,0.0002480777,0.00005320979,0.002581286,0.0005689033,0.0007761293,0.0001498623,0.0001329988],"category_scores_gemma":[0.0006325748,0.0001725428,0.00007054151,0.00006373414,0.0003260264,0.0005567118,0.0001789056,0.0001618097,0.000005772604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008244084,"about_ca_system_score_gemma":0.00007206663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00276497,"about_ca_topic_score_gemma":0.005195668,"domain_scores_codex":[0.9982867,0.00009902787,0.0003268641,0.0003465214,0.0003721073,0.0005688337],"domain_scores_gemma":[0.9988163,0.0002454687,0.0003943103,0.0003829899,0.00006769352,0.00009322789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007893682,0.0001111961,0.5909766,0.0002031636,0.0001710926,0.00001318768,0.03507777,3.009654e-7,0.0005610075,0.06206439,0.0123417,0.2984007],"study_design_scores_gemma":[0.001527579,0.0001229043,0.009564982,0.0001812506,0.00006866195,6.572892e-7,0.09640749,0.0001750462,0.0004224615,0.008655181,0.8823872,0.000486653],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.72825,0.001001191,0.001049521,0.05657787,0.001396061,0.001829289,0.0001138653,0.0007973956,0.2089848],"genre_scores_gemma":[0.995305,0.0003393733,0.001331786,0.0003754325,0.0002036138,0.00008607461,0.00001565038,0.00002038225,0.002322698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8700454,"threshold_uncertainty_score":0.9987172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111178206413335,"score_gpt":0.3853024901294737,"score_spread":0.2741242837161387,"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."}}