{"id":"W7109611523","doi":"10.5683/sp3/dsufk3","title":"Replication Data and Code for: Discrimination and the Fiscal Benefits of Immigration","year":2025,"lang":"","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Replication (statistics); Replicate; Immigration; Code (set theory); Data file; Fiscal year","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003364735,0.001530913,0.001259476,0.004138596,0.00124535,0.003376418,0.002797182,0.001834057,0.1540914],"category_scores_gemma":[0.02716985,0.0009641447,0.001598459,0.007762291,0.000568126,0.001723947,0.002244623,0.00245372,0.09225482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002338015,"about_ca_system_score_gemma":0.004439203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08238501,"about_ca_topic_score_gemma":0.1059206,"domain_scores_codex":[0.9971159,0.00061357,0.0003859738,0.0006799171,0.0007773877,0.0004272854],"domain_scores_gemma":[0.9872649,0.003180662,0.001616191,0.003061669,0.004154398,0.0007222538],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005095043,0.00001188874,0.001327256,0.0001412909,0.00002083473,0.000008826096,0.00002246706,0.0001020584,0.00001870624,0.0005752989,0.9965305,0.00118995],"study_design_scores_gemma":[0.000757655,0.00003275674,0.02390172,0.0004035185,0.00007649699,0.00006569445,0.0002440096,0.0003831341,0.0003082487,0.002650228,0.9711031,0.00007351201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002065161,0.00003007496,0.00008910193,0.0001327769,0.00006259677,0.00002808041,0.9976566,0.000185699,0.001608597],"genre_scores_gemma":[0.002275757,0.00004954802,0.0005017596,0.0001743716,0.0000427596,0.0003796134,0.9918006,0.0002930576,0.004482562],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9966353,"threshold_uncertainty_score":0.5154871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210675623055592,"score_gpt":0.3233662543968827,"score_spread":0.2812594981663268,"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."}}