{"id":"W4377091894","doi":"10.1016/j.evalprogplan.2023.102318","title":"Mapping the evaluation capacity building landscape: A bibliometric analysis of scholarly communities and themes","year":2023,"lang":"en","type":"article","venue":"Evaluation and Program Planning","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Ottawa","funders":"","keywords":"Scholarship; Bibliometrics; Cover (algebra); Regional science; Sociology; Political science; Library science; Engineering; Computer science","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":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0432756,0.000125935,0.0002820112,0.02843608,0.0006083488,0.001234217,0.000292429,0.00006783288,0.0004574986],"category_scores_gemma":[0.003247499,0.00008109975,0.00007880374,0.09017033,0.0001243323,0.001029177,0.000133635,0.0002004726,0.000008908002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002677675,"about_ca_system_score_gemma":0.0001201533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006723902,"about_ca_topic_score_gemma":0.00004733735,"domain_scores_codex":[0.9940853,0.001406105,0.0006152038,0.0002496372,0.003444371,0.0001993941],"domain_scores_gemma":[0.9951001,0.002427261,0.000412694,0.0003706648,0.001627259,0.00006202467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000007683697,0.0000163126,0.4084915,0.000007631666,0.0001635411,8.501734e-8,0.0113719,0.006341934,0.00009524641,0.0002165674,0.0002202698,0.5730673],"study_design_scores_gemma":[0.0002512099,0.0000379747,0.500823,0.00002242534,0.0002032012,8.64299e-7,0.01430797,0.481927,0.0000162857,0.001534984,0.0008187661,0.00005633341],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937729,0.002215658,0.0007172811,0.0007919553,0.000106405,0.0007221942,0.00001034296,0.0000635881,0.001599709],"genre_scores_gemma":[0.9984071,0.0001757785,0.0009892688,0.0001053865,0.00002865961,0.0001683445,0.00007667302,0.00000573126,0.0000430525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.573011,"threshold_uncertainty_score":0.9998026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6130985989473278,"score_gpt":0.5573777674261452,"score_spread":0.05572083152118257,"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."}}