{"id":"W3125212608","doi":"","title":"Technological Learning and Organizational Context: Fit and Performance in SMEs","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Gestalt psychology; Coherence (philosophical gambling strategy); Context (archaeology); Organizational learning; Humanities; Psychology; Knowledge management; Perception; Computer science; Epistemology; Mathematics; Philosophy; Geography; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002566198,0.0002886538,0.0003632663,0.001490748,0.0006236174,0.003052,0.0003886885,0.001349642,0.002973031],"category_scores_gemma":[0.02184372,0.0001242046,0.0003454106,0.00190498,0.000841859,0.001729663,0.002488537,0.0004855759,0.0006733994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000518115,"about_ca_system_score_gemma":0.0004018295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009818466,"about_ca_topic_score_gemma":0.001430649,"domain_scores_codex":[0.9981393,0.001008713,0.0001589461,0.0001293298,0.0003803146,0.0001834595],"domain_scores_gemma":[0.985465,0.00832573,0.002988526,0.0008519919,0.0009819752,0.001386866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001048443,0.0009778715,0.9268974,0.0001164186,0.0001926262,0.0005194181,0.005894154,0.005830223,0.003416629,0.002957998,0.0003221544,0.05182667],"study_design_scores_gemma":[0.0000298912,0.001047066,0.979753,0.0000437406,0.00004723239,0.0003376642,0.004336543,0.007207598,0.0008482423,0.005761118,0.000549706,0.00003817824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976065,0.00009835111,0.0003032645,0.00007487062,0.000002780463,0.000005413646,0.00001961143,0.00000691686,0.001882379],"genre_scores_gemma":[0.9997341,0.00002701531,0.00007890872,0.000008428809,0.000003190453,0.00000400579,0.00002438434,0.000002815879,0.0001170017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003052,"threshold_uncertainty_score":0.01357156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264829278628477,"score_gpt":0.2724138670577927,"score_spread":0.245930939194945,"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."}}