{"id":"W4398370825","doi":"10.7910/dvn/krogfv","title":"Replication Data for: Service Representation in a Federal System: A Field Experiment","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Representation (politics); Field (mathematics); Service (business); Computer science; Business; Political science; Mathematics; Statistics","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":[0.01708056,0.001297735,0.00135683,0.002871044,0.00226239,0.002667369,0.003257316,0.002045108,0.1273371],"category_scores_gemma":[0.1078978,0.001006559,0.001702495,0.005904256,0.001296601,0.002563641,0.002426534,0.003716687,0.06336065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002587036,"about_ca_system_score_gemma":0.007253394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01920758,"about_ca_topic_score_gemma":0.03483355,"domain_scores_codex":[0.9883189,0.004268729,0.001733888,0.001958824,0.003056875,0.0006627624],"domain_scores_gemma":[0.9123522,0.02522819,0.004836426,0.03454943,0.0205674,0.002466331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002383916,0.00007234891,0.001097693,0.0002092635,0.00003771024,0.00001766027,0.0000622893,0.0001174449,0.00007382801,0.0009389925,0.9950615,0.002072864],"study_design_scores_gemma":[0.002837348,0.0001363317,0.01786962,0.0004636689,0.0001409299,0.00009294338,0.0004188244,0.0005105007,0.0009040889,0.006122908,0.9703836,0.0001191995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001483693,0.00004951069,0.001208343,0.0007566826,0.0004076857,0.0006725737,0.9904819,0.000892519,0.004047032],"genre_scores_gemma":[0.008332937,0.00006565517,0.004484184,0.0006800885,0.0001425641,0.007274778,0.9693421,0.001038472,0.00863918],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1273371,"threshold_uncertainty_score":0.4259851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2563692904441717,"score_gpt":0.45251321404726,"score_spread":0.1961439236030883,"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."}}