{"id":"W1792932638","doi":"10.1186/s40175-015-0033-7","title":"Integrating mobile phone technologies into labor-market intermediation: a multi-treatment experimental design","year":2015,"lang":"en","type":"article","venue":"IZA Journal of Labor & Development","topic":"ICT Impact and Policies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada; University of Ottawa; University of Guelph; Syracuse University; Inter-American Development Bank","keywords":"Intermediation; Mobile phone; Matching (statistics); Phone; Business; Intermediary; Labour economics; Term (time); Economics; Demographic economics; Marketing; Computer science; Telecommunications; Finance; Medicine","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":[0.01132559,0.001720854,0.002267925,0.001109308,0.001304976,0.002329909,0.002531721,0.004526962,0.02795518],"category_scores_gemma":[0.0261728,0.001022054,0.001639449,0.0008009822,0.003242337,0.00168657,0.002023532,0.003172301,0.001663786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001939843,"about_ca_system_score_gemma":0.002553895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006926226,"about_ca_topic_score_gemma":0.0004408,"domain_scores_codex":[0.9864725,0.008791615,0.0008534744,0.001427422,0.001235999,0.001218999],"domain_scores_gemma":[0.9666255,0.02084475,0.006817527,0.003645118,0.0009781973,0.001088899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.506081,0.2823511,0.01406917,0.003854506,0.002103757,0.0006300926,0.002502704,0.02383405,0.03390438,0.05118588,0.00394215,0.07554123],"study_design_scores_gemma":[0.2790678,0.479196,0.03256802,0.0007177683,0.003460486,0.0002099555,0.001618514,0.09127697,0.02863769,0.06672617,0.01605668,0.0004639455],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9419643,0.0002095782,0.01828503,0.0008173654,0.0007779546,0.03046834,0.001022477,0.0001735046,0.006281411],"genre_scores_gemma":[0.8425201,0.0002078407,0.028743,0.0007754214,0.0004065152,0.1155408,0.0003204203,0.00003142864,0.01145454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02795518,"threshold_uncertainty_score":0.09351939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470429146234865,"score_gpt":0.2803054392404082,"score_spread":0.2556011477780596,"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."}}