{"id":"W4416753157","doi":"","title":"Deliberation and Policy Outcomes: Evidence from the Textual Analysis of FOMC Transcripts *","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Deliberation; Construct (python library); Tone (literature); Dissent; Predictive power; Natural (archaeology)","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":[0.01549242,0.0004033493,0.0003573546,0.0033106,0.001206117,0.002426984,0.0008102263,0.001110315,0.007932668],"category_scores_gemma":[0.2384014,0.0003119064,0.0002965525,0.007220817,0.002018293,0.00222138,0.002074188,0.001143337,0.001423288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693033,"about_ca_system_score_gemma":0.00167407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009457493,"about_ca_topic_score_gemma":0.006435738,"domain_scores_codex":[0.9780912,0.01616663,0.0009486554,0.001080692,0.003062487,0.000650377],"domain_scores_gemma":[0.5523117,0.3668434,0.05563693,0.008281072,0.01479581,0.002131116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004254922,0.001406706,0.4939975,0.004915795,0.0006721172,0.002383595,0.2046275,0.006110978,0.0050244,0.01977948,0.02080547,0.2360215],"study_design_scores_gemma":[0.0001898495,0.0004883909,0.8489026,0.001791729,0.0001743806,0.0004542353,0.06834947,0.01054415,0.004340847,0.02124217,0.04331296,0.000209153],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521737,0.0008120008,0.01030661,0.003599548,0.0001035947,0.0003924438,0.009025027,0.000116574,0.0234705],"genre_scores_gemma":[0.9889982,0.0005294702,0.004295704,0.0002795878,0.00009646738,0.0006110987,0.003547362,0.00005572284,0.00158648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01549242,"threshold_uncertainty_score":0.08193272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04523781637061822,"score_gpt":0.3458457691964065,"score_spread":0.3006079528257883,"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."}}