{"id":"W2034578233","doi":"10.1007/s10460-013-9438-6","title":"Farmer innovation diffusion via network building: a case of winter greenhouse diffusion in China","year":2013,"lang":"en","type":"article","venue":"Agriculture and Human Values","topic":"Agricultural Innovations and Practices","field":"Agricultural and Biological Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; Natural Environment Research Council; Sight Research UK; International Development Research Centre","keywords":"Government (linguistics); Business; China; Innovation diffusion; Diffusion of innovations; Greenhouse gas; Process (computing); Greenhouse; Industrial organization; Intervention (counseling); Social network analysis; Marketing; Environmental economics; Knowledge management; Economic growth; Economics; Political science; Computer science; Social capital","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002012128,0.000206157,0.0002418805,0.00003604841,0.0004002672,0.000108152,0.0001267966,0.0001543257,0.0004651175],"category_scores_gemma":[0.00002053883,0.00006528639,0.00005194156,0.001021937,0.00006771053,0.0005277239,0.0001136525,0.0002103562,0.000007534486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001519347,"about_ca_system_score_gemma":0.000001573938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005074982,"about_ca_topic_score_gemma":0.002319399,"domain_scores_codex":[0.998772,0.00008372478,0.000454305,0.0002958278,0.0001444928,0.0002496613],"domain_scores_gemma":[0.999318,0.00008843568,0.0002845623,0.0000565421,0.0002055062,0.00004696431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001433546,0.0002634074,0.05555229,0.00002100308,0.00002331256,0.00002570931,0.0006802203,0.00001050096,0.8903415,0.005122028,0.008444261,0.03950144],"study_design_scores_gemma":[0.0002068125,0.0002711899,0.9871662,0.00007309995,0.00002134096,0.0001598328,0.0007852813,0.0000487601,0.001282284,0.00520163,0.004514388,0.0002691585],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976095,0.0001711213,0.000009212044,0.0009101804,0.00006429657,0.0003714151,0.000005582991,0.00004421298,0.0008144582],"genre_scores_gemma":[0.9983838,0.00004614201,0.0002217881,0.0001728226,0.0003764851,0.00003026672,0.00009695057,0.000001407507,0.0006703138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9316139,"threshold_uncertainty_score":0.7671888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655083046282416,"score_gpt":0.2475576300721326,"score_spread":0.2310067996093084,"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."}}