{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.1332318,0.0001039306,0.0002738225,0.05107069,0.0003693711,0.002595573,0.00120872,0.0001267897,0.0002135888],"category_scores_gemma":[0.05246375,0.00005599055,0.00009869773,0.07561777,0.0005842383,0.0020177,0.0002857632,0.0007195262,0.00007069752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098094,"about_ca_system_score_gemma":0.003106095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003622044,"about_ca_topic_score_gemma":0.00017383,"domain_scores_codex":[0.9847255,0.001895304,0.001441766,0.0004681135,0.01056546,0.0009038725],"domain_scores_gemma":[0.9671531,0.0142386,0.0004720061,0.0004994877,0.01695354,0.0006832505],"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":[0.0004642472,0.0009336033,0.2529215,0.00004707687,0.00002528691,0.0001170565,0.0009198682,0.0000303086,0.07278461,0.01353393,0.00959731,0.6486253],"study_design_scores_gemma":[0.002830144,0.007101149,0.5868688,0.0002928764,0.000006146542,0.0003691586,0.01462114,0.00276367,0.01768649,0.3356008,0.03149206,0.0003676063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703491,0.001330903,0.02213247,0.002460836,0.00008357134,0.0005818848,0.00001496024,0.000006197168,0.00304011],"genre_scores_gemma":[0.9976897,0.0008442994,0.000522421,0.000002347609,0.0001814739,0.0000274877,0.000001305074,0.000008848487,0.0007221535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6482576,"threshold_uncertainty_score":0.9984398,"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."}}