{"id":"W4366448482","doi":"10.3138/cjpe.020.005","title":"The Delphi Technique as a Method for Increasing Inclusion in the Evaluation Process","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Program Evaluation","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Delphi method; Delphi; Compromise; Inclusion (mineral); Process (computing); Set (abstract data type); Evaluation methods; Psychology; Economic Justice; Representation (politics); Public relations; Management science; Sociology; Engineering ethics; Computer science; Social psychology; Political science; Engineering; Social science; Artificial intelligence; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3784713,0.002256457,0.002065032,0.01004077,0.007172154,0.006095967,0.003204141,0.002380268,0.01040698],"category_scores_gemma":[0.3295668,0.001876949,0.001315588,0.006516421,0.009790544,0.006586414,0.01576302,0.006411629,0.002252146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004406443,"about_ca_system_score_gemma":0.01314928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280438,"about_ca_topic_score_gemma":0.002470285,"domain_scores_codex":[0.34213,0.6042072,0.01645138,0.003658949,0.03163965,0.001912832],"domain_scores_gemma":[0.5753041,0.3326742,0.009167216,0.02245212,0.05754546,0.002856868],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00120687,0.0008302667,0.002169311,0.005599879,0.0002046603,0.0008176289,0.2907682,0.002910637,0.01730672,0.07881551,0.01720865,0.5821616],"study_design_scores_gemma":[0.002313374,0.006126159,0.01368456,0.01712961,0.0004158072,0.002469053,0.2202808,0.03464665,0.03266539,0.2772962,0.3914678,0.001504485],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03981146,0.0005735795,0.8618709,0.004977835,0.0008772229,0.05976003,0.0001942406,0.0005953332,0.03133936],"genre_scores_gemma":[0.07192934,0.0003604925,0.8722822,0.000637926,0.0001371416,0.05204704,0.00005236096,0.0001583515,0.002395155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3784713,"threshold_uncertainty_score":0.7664556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3689523270921288,"score_gpt":0.6178187315297932,"score_spread":0.2488664044376644,"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."}}