{"id":"W4296919084","doi":"10.15353/joci.v18i2.4828","title":"Digital Literacy and Long-Term Labor Outcomes","year":2022,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"ICT Impact and Policies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Laptop; Productivity; Treatment and control groups; Intervention (counseling); Psychology; Difference in differences; Baseline (sea); Significant difference; Medical education; Impact evaluation; Term (time); Political science; Medicine; Economic growth; Economics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005551137,0.000145639,0.0001023871,0.0005762717,0.0004942418,0.0008438331,0.0002124411,0.0002100114,0.003696974],"category_scores_gemma":[0.002326783,0.00005617923,0.0001451451,0.0004514426,0.0003939542,0.0002947825,0.0007353275,0.000414327,0.0003085189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006917943,"about_ca_system_score_gemma":0.0005952802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0121062,"about_ca_topic_score_gemma":0.02882851,"domain_scores_codex":[0.999716,0.00008327168,0.00001574427,0.00003296575,0.00005377818,0.00009828059],"domain_scores_gemma":[0.9984896,0.0002300049,0.000708265,0.00007287375,0.000146015,0.000353258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009133487,0.0006993194,0.9727196,0.0000402861,0.0000270417,0.0001166609,0.000924186,0.0001224015,0.0004497783,0.0003201821,0.0002607632,0.02422849],"study_design_scores_gemma":[0.000001976406,0.0001022108,0.9983924,0.00001381781,0.000006752403,0.0000188781,0.0006703537,0.00005573161,0.0001360743,0.00007119933,0.0005290841,0.000001547604],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967692,0.0001221169,0.00004175414,0.000129027,0.000003241992,0.00001133159,0.0001261425,0.000002799897,0.002794376],"genre_scores_gemma":[0.9990795,0.00006235869,0.00003277763,0.00001520739,0.000002787001,0.000006534085,0.00006473132,5.498214e-7,0.0007355359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0121062,"threshold_uncertainty_score":0.02407151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540219664817706,"score_gpt":0.2719735152276275,"score_spread":0.2565713185794505,"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."}}