{"id":"W2980182125","doi":"10.13140/rg.2.2.29470.59208","title":"Measuring Uncertainty at the Regional Level Using Newspaper Text","year":2019,"lang":"en","type":"article","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Regional resilience and development","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Newspaper; Unemployment; Download; Econometrics; Vector autoregression; Inflation (cosmology); Index (typography); Order (exchange); Composite index; Measure (data warehouse); Computer science; Regional science; Economics; Macroeconomics; Geography; Business; Data mining; Composite indicator; Advertising; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002763084,0.0001875002,0.0002371446,0.0001457855,0.002984391,0.00004537257,0.0003518679,0.0001106893,0.0002340448],"category_scores_gemma":[0.00003112621,0.0001765943,0.0001879326,0.0002264482,0.0001881081,0.0003006156,0.000248549,0.000156329,0.0008896979],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004711608,"about_ca_system_score_gemma":0.0004161489,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01358352,"about_ca_topic_score_gemma":0.00111628,"domain_scores_codex":[0.9986426,0.00002423295,0.0003235363,0.0004574292,0.0002111989,0.0003410676],"domain_scores_gemma":[0.9991137,0.00007738434,0.0002424101,0.0003539255,0.00007040139,0.0001421359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005857455,0.0001834928,0.5228255,0.00005426948,0.0003897156,0.0003273495,0.01126846,0.08802184,0.0079003,0.3607823,0.00554656,0.002114423],"study_design_scores_gemma":[0.001540008,0.00005304662,0.35137,0.00008813317,0.00002727537,0.0008308248,0.004592503,0.008737729,0.001206651,0.004658689,0.626174,0.000721191],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315819,0.007062415,0.0003696432,0.001144908,0.0004198366,0.0002158475,0.00002482644,0.00002960134,0.05915099],"genre_scores_gemma":[0.9408942,0.0002220942,0.0004372787,0.0005455095,0.0000798853,0.000007003343,0.00001433015,0.00001377411,0.05778589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6206274,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04232601031627575,"score_gpt":0.1758135863268258,"score_spread":0.1334875760105501,"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."}}