{"id":"W3140429884","doi":"","title":"Montreal’s Technological and Cultural Clusters Strategy: The Case of the Multimedia, and Film and Audiovisual Production","year":2010,"lang":"en","type":"article","venue":"Chapters","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Production (economics); Economy; Economic geography; Political science; Business; Economics; Macroeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001706771,0.0006640701,0.0001641933,0.001670099,0.01258233,0.009571836,0.00212722,0.002618385,0.01701822],"category_scores_gemma":[0.003692635,0.000267627,0.0003579944,0.003911945,0.006045863,0.0031559,0.003056695,0.001715099,0.00091615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04482492,"about_ca_system_score_gemma":0.058217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9033455,"about_ca_topic_score_gemma":0.9438025,"domain_scores_codex":[0.9982653,0.0003702368,0.00002249478,0.0001528363,0.0005908817,0.0005982472],"domain_scores_gemma":[0.9987081,0.0002285869,0.00006839365,0.0001068052,0.0003465088,0.0005417072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003085529,0.0000259237,0.001411947,0.00009391169,0.00001290883,0.0005346278,0.004675428,0.0007635953,0.0003446566,0.8274664,0.1182361,0.0464035],"study_design_scores_gemma":[0.00002155276,0.00003097341,0.003812895,0.00009918952,0.00001176494,0.0001192561,0.003704599,0.0005900233,0.0003244792,0.01753449,0.9737203,0.00003062211],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02309553,0.005034444,0.003585193,0.05982057,0.0004903009,0.0002706342,0.00034772,0.0001592976,0.9071963],"genre_scores_gemma":[0.5191941,0.004566041,0.005486177,0.00796724,0.0002855657,0.0002317466,0.0002620352,0.000151095,0.4618559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09665453,"threshold_uncertainty_score":0.3252291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236614506967476,"score_gpt":0.2843486433973864,"score_spread":0.2519824983277116,"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."}}