{"id":"W4416799555","doi":"10.1109/snpd65828.2025.11253447","title":"Reducing Financial Debt and Illiteracy in Canadian Populations Using Machine Learning Prediction Models","year":2025,"lang":"","type":"article","venue":"","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Bad debt; Pipeline (software); Functional illiteracy; Autoregressive integrated moving average; Debt; Predictive modelling; Financial modeling; Heuristic","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001313718,0.0006866411,0.0004626428,0.001411728,0.0009523148,0.001115558,0.001168783,0.0005454827,0.002252337],"category_scores_gemma":[0.007464902,0.0002777649,0.000645628,0.001805641,0.0002997352,0.00065876,0.0005106206,0.0008869003,0.0004544569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006055877,"about_ca_system_score_gemma":0.007311051,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9231392,"about_ca_topic_score_gemma":0.9342936,"domain_scores_codex":[0.9996183,0.0001022772,0.00001981976,0.0001033731,0.00007453879,0.00008163191],"domain_scores_gemma":[0.9981041,0.0008789817,0.00015145,0.0001632534,0.0005424863,0.0001597224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004472102,0.0004508145,0.5771543,0.0001564705,0.0002755602,0.0002567642,0.0006950932,0.1688408,0.00100094,0.002814722,0.02015876,0.2277486],"study_design_scores_gemma":[0.00004649956,0.0001166526,0.1266277,0.00007173669,0.0001334073,0.00008842861,0.0006314171,0.8611262,0.001146016,0.002514897,0.007425913,0.00007117802],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249249,0.001494112,0.0418958,0.004056814,0.0000928736,0.0002419803,0.01517102,0.00165674,0.01046569],"genre_scores_gemma":[0.9611852,0.0004489931,0.02833565,0.0002601012,0.00002258742,0.00004934148,0.00670097,0.00004164116,0.002955549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07686085,"threshold_uncertainty_score":0.154627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132835745993488,"score_gpt":0.2393321660337939,"score_spread":0.218003808573859,"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."}}