{"id":"W2223090851","doi":"10.3386/w21835","title":"Incentive Design in Education: An Empirical Analysis","year":2015,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"School Choice and Performance","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Incentive; Computer science; Psychology; Data science; Economics; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0484775,0.0006399095,0.001630945,0.00271508,0.001394596,0.002859057,0.002218862,0.002572068,0.01643593],"category_scores_gemma":[0.197477,0.0005861533,0.001498309,0.004826645,0.003897219,0.003674418,0.003449294,0.003805308,0.001187129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00310013,"about_ca_system_score_gemma":0.002449763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00508692,"about_ca_topic_score_gemma":0.003062567,"domain_scores_codex":[0.9634172,0.02949376,0.00115858,0.001842782,0.002400093,0.001687482],"domain_scores_gemma":[0.5894659,0.3671413,0.02191324,0.01348706,0.005759224,0.002233241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00226491,0.007054669,0.4788756,0.00116395,0.001117767,0.0005792601,0.004679845,0.05527214,0.0008684883,0.2763559,0.009528287,0.1622393],"study_design_scores_gemma":[0.001443982,0.003161448,0.4512377,0.0007597792,0.001070935,0.000428907,0.007114058,0.3334782,0.001987136,0.1820457,0.01705994,0.0002121056],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786842,0.001309292,0.1049849,0.002852727,0.00006822541,0.001136184,0.0008762426,0.000174182,0.009913987],"genre_scores_gemma":[0.9883729,0.0002123086,0.008881301,0.0001633339,0.00004915314,0.0003666137,0.0002412292,0.00002233189,0.001690839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0484775,"threshold_uncertainty_score":0.2563766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5584150414728961,"score_gpt":0.6203085891547596,"score_spread":0.0618935476818635,"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."}}