{"id":"W2616004446","doi":"","title":"Supporting Knowledge Mobilization and Research Impact Strategies in Grant Applications","year":2016,"lang":"en","type":"article","venue":"Journal of Research Administration","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Excellence; Scholarship; Citation impact; Political science; Social research; Citation; Public relations; Grantsmanship; Sociology; Social science; Public administration; Library science; Higher education","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"incentives","study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3550978,0.002229256,0.001770436,0.0156451,0.01363632,0.05141131,0.008277542,0.01427361,0.03863518],"category_scores_gemma":[0.5422704,0.001487871,0.002178826,0.01256335,0.01628966,0.03284224,0.04493035,0.01004461,0.01182589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0258497,"about_ca_system_score_gemma":0.1097884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058888,"about_ca_topic_score_gemma":0.003510227,"domain_scores_codex":[0.6400021,0.2599458,0.01891223,0.01468593,0.04495179,0.02150214],"domain_scores_gemma":[0.3546723,0.4877801,0.03381402,0.03692041,0.05418892,0.0326242],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003324868,0.001642945,0.01035698,0.002837239,0.0001788694,0.0006717042,0.03589667,0.00464044,0.001150543,0.4681258,0.03817192,0.4359943],"study_design_scores_gemma":[0.0006910486,0.0007190145,0.007735108,0.006105926,0.0002288765,0.0002961433,0.03081578,0.007391511,0.002343195,0.6789777,0.2644156,0.0002800891],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.07110523,0.004123533,0.1608531,0.1701346,0.002022224,0.01604077,0.0006919322,0.001905895,0.5731227],"genre_scores_gemma":[0.7687624,0.002543379,0.1680533,0.01623072,0.001342486,0.02249452,0.0006120929,0.0005103032,0.01945091],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6449022,"threshold_uncertainty_score":0.7952793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7008402576772212,"score_gpt":0.7181724566775245,"score_spread":0.01733219900030325,"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."}}