{"id":"W2103013209","doi":"10.1007/s11192-009-0020-3","title":"Matrices of science and technology interactions and patterns of structured growth: implications for development","year":2009,"lang":"en","type":"article","venue":"Scientometrics","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; International Development Research Centre","keywords":"Dimension (graph theory); Articulation (sociology); Computer science; Component (thermodynamics); Key (lock); Scientific literature; Regional science; Data science; Sociology; Political science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006003584,0.00005703051,0.0001705942,0.00450086,0.0001203204,0.00002816473,0.0002097918,0.00005415455,0.000005422793],"category_scores_gemma":[0.0009311485,0.00005896534,0.00001184923,0.006246259,0.0003083748,0.0001916394,0.00006845313,0.00004930275,7.559952e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005920239,"about_ca_system_score_gemma":0.00002942444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000621638,"about_ca_topic_score_gemma":0.000002318343,"domain_scores_codex":[0.9991687,9.019705e-7,0.0004069901,0.0002529912,0.00003301316,0.0001374178],"domain_scores_gemma":[0.9990955,0.00005042479,0.0003480884,0.0001363988,0.0003379865,0.00003157497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000001112578,0.00002692539,0.144526,0.00001460975,0.000003782661,1.035339e-8,0.0000502418,4.111628e-7,0.001446827,0.8344076,0.00001548974,0.01950702],"study_design_scores_gemma":[0.0002373504,0.0001002793,0.6831429,0.000008368225,0.000002848495,0.000002637978,0.00008451492,0.00019471,0.01280437,0.3013046,0.002016803,0.0001006172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810764,0.0004074795,0.01651441,0.001149251,0.00008134004,0.0001674228,0.00007876622,0.00001988226,0.0005050513],"genre_scores_gemma":[0.9869646,0.00007008525,0.01288606,0.00003253387,0.000004455745,0.000008915634,0.000003111395,0.000002279066,0.00002796098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.538617,"threshold_uncertainty_score":0.4016071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05936876136323518,"score_gpt":0.2929687513041478,"score_spread":0.2335999899409126,"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."}}