{"id":"W3122250218","doi":"10.20381/ruor-25609","title":"Estimating Labour Market Transitions and Continuations using Repeated Cross Sectional Data","year":2007,"lang":"en","type":"preprint","venue":"uO Research (University of Ottawa)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Wilfrid Laurier University","funders":"","keywords":"Continuation; Inference; Current Population Survey; Cross-sectional data; Econometrics; Cross-sectional study; Population; Computer science; Statistics; Mathematics; Medicine; Artificial intelligence","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":[0.01762844,0.0001517598,0.0003302469,0.001523323,0.001251095,0.0004152418,0.001557313,0.0003140184,0.0007355282],"category_scores_gemma":[0.002474443,0.0001750067,0.0001141385,0.00127249,0.0007827507,0.0006065574,0.001474465,0.0009235901,0.0000138334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007597295,"about_ca_system_score_gemma":0.0003794124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001001346,"about_ca_topic_score_gemma":0.001506149,"domain_scores_codex":[0.9949329,0.0006933201,0.0005199065,0.001017799,0.002453842,0.0003822304],"domain_scores_gemma":[0.9939857,0.001540749,0.0003842261,0.001291247,0.002600731,0.000197339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002076507,0.0007574207,0.7033592,0.0007039518,0.0007415309,0.0003883008,0.007131612,0.2054738,0.001727353,0.02067083,0.01604873,0.0427897],"study_design_scores_gemma":[0.0003425104,0.00002284835,0.2287583,0.0000855008,0.00002317032,0.00001484516,0.002357941,0.7557826,0.000002997494,0.01097612,0.001491213,0.0001419394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7233309,0.00004187038,0.2670436,0.0004938899,0.0002042434,0.0002780927,0.00130915,0.00004331162,0.007254972],"genre_scores_gemma":[0.9173866,0.00004795982,0.07824968,0.00001091919,0.00007815305,4.182103e-7,0.000370806,0.0000141152,0.003841385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5503088,"threshold_uncertainty_score":0.9622536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.476174092948657,"score_gpt":0.4962642200186502,"score_spread":0.02009012706999325,"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."}}