{"id":"W4410115952","doi":"10.1109/emr.2025.3567192","title":"Understanding and Deobfuscating Textual Data: Managerial Insights and Computational Challenges","year":2025,"lang":"en","type":"article","venue":"IEEE Engineering Management Review","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Business; Data science; Computer science; Industrial organization; Knowledge management; Management science; Process management; Econometrics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002665402,0.0001849046,0.0003565467,0.0003295511,0.0001377166,0.0002839479,0.0006870172,0.00002958904,0.00001350703],"category_scores_gemma":[0.0003051,0.0001582834,0.00003056437,0.0004676977,0.00006290423,0.0005081097,0.001024656,0.00009323433,0.00001873332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004356113,"about_ca_system_score_gemma":0.000007768327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004844023,"about_ca_topic_score_gemma":0.00001173889,"domain_scores_codex":[0.99792,0.00009700804,0.000560293,0.0006797867,0.0005478547,0.0001950961],"domain_scores_gemma":[0.9985025,0.0005436024,0.0001129689,0.0007487856,0.00002910823,0.00006304218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003520221,0.00002482238,0.00001253777,0.004727514,0.0001656004,0.00001815246,0.0001014983,0.001689808,0.000001387649,0.7728456,0.02662961,0.19378],"study_design_scores_gemma":[0.000613925,0.0000235769,0.00225024,0.004381966,0.0002578328,0.00000487537,0.0009657314,0.04153285,0.000001083337,0.03901654,0.9105354,0.0004159702],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0004685696,0.1379914,0.831831,0.005782065,0.0009707003,0.001157237,0.00002855583,0.0001736877,0.02159669],"genre_scores_gemma":[0.3864602,0.5655891,0.04060252,0.00397887,0.0002837084,0.0001319206,0.000202367,0.00005013377,0.00270113],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8839058,"threshold_uncertainty_score":0.645461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3507618376440356,"score_gpt":0.3984480790666918,"score_spread":0.04768624142265621,"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."}}