{"id":"W4393901348","doi":"10.3138/cjpe-2024-0014","title":"Exploring the Edges: Identifying the Next Generation of Evaluation Capacity Building Research and Practice Through Adjacency","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Program Evaluation","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University","funders":"","keywords":"Adjacency list; Computer science; Data science; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":["metaresearch"],"domain":"methods","study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06498821,0.0008146887,0.001477704,0.01086026,0.004245006,0.02200382,0.003127838,0.003650332,0.009543638],"category_scores_gemma":[0.1181104,0.0006231148,0.001189389,0.01412975,0.02254685,0.04080113,0.01084415,0.005259924,0.0008950419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01061182,"about_ca_system_score_gemma":0.02694483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008546238,"about_ca_topic_score_gemma":0.01249566,"domain_scores_codex":[0.9540274,0.03679529,0.001547027,0.001733135,0.003966273,0.001930809],"domain_scores_gemma":[0.7541959,0.213877,0.005707671,0.008132271,0.01465294,0.003434202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007761407,0.00009108432,0.003458435,0.007235765,0.00008961994,0.0001962785,0.02757458,0.0008323827,0.0003027312,0.620719,0.007982606,0.33144],"study_design_scores_gemma":[0.00003501838,0.0001254584,0.003121228,0.02521388,0.0001769002,0.0001883898,0.0644322,0.002001971,0.0008281801,0.5895485,0.3142642,0.0000640764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06375046,0.227202,0.2007023,0.2138984,0.002386248,0.001166709,0.0005083172,0.0005005656,0.289885],"genre_scores_gemma":[0.7332187,0.1064688,0.1379213,0.01476837,0.0008200643,0.001337896,0.0003526807,0.0003075385,0.004804769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06498821,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9617855589426995,"score_gpt":0.6449060188564266,"score_spread":0.3168795400862728,"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."}}