{"id":"W4400991456","doi":"10.69554/zkbp6430","title":"Collaborating with research: Leveraging government grants to increase industry support","year":2022,"lang":"en","type":"article","venue":"Journal of education advancement & marketing.","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Government (linguistics); Business; Public administration; Knowledge management; Process management; Political science; Public relations; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04660201,0.0007680865,0.0006462713,0.005536947,0.01278498,0.0199264,0.005115774,0.00591876,0.008281575],"category_scores_gemma":[0.1141879,0.0006480424,0.0008900274,0.005468556,0.008899124,0.01149278,0.03431279,0.005805991,0.002000018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01381793,"about_ca_system_score_gemma":0.09350447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04735151,"about_ca_topic_score_gemma":0.06703202,"domain_scores_codex":[0.9315033,0.03452722,0.001878768,0.003002569,0.0121741,0.0169141],"domain_scores_gemma":[0.892897,0.04694125,0.008637642,0.01509557,0.01277806,0.0236505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003547349,0.0006804707,0.03660384,0.0005107306,0.000208027,0.004228103,0.03977411,0.004884108,0.004854122,0.4031574,0.04076833,0.4639759],"study_design_scores_gemma":[0.0006354869,0.001095846,0.03595931,0.001524112,0.0002771722,0.002252509,0.05805102,0.00758422,0.00467785,0.2961265,0.5913341,0.0004818985],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3307158,0.003647543,0.100448,0.1297929,0.001335114,0.003080629,0.0002212012,0.00147479,0.4292838],"genre_scores_gemma":[0.9490418,0.0007932584,0.0341657,0.003717304,0.0002191062,0.0006604003,0.00008654714,0.0001539741,0.0111619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04735151,"threshold_uncertainty_score":0.2464579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07306098637151637,"score_gpt":0.3304899637660277,"score_spread":0.2574289773945114,"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."}}