{"id":"W2078182602","doi":"10.4018/jhcitp.2011010104","title":"The Determinants of Information Technology Wages","year":2011,"lang":"en","type":"article","venue":"International Journal of Human Capital and Information Technology Professionals","topic":"ICT Impact and Policies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Salary; Human capital; Marital status; Binomial regression; Certification; Compensation (psychology); Logistic regression; Human capital theory; Economics; Demographic economics; Work experience; Work (physics); Labour economics; Business; Econometrics; Psychology; Management; Statistics; Social psychology; Engineering; Sociology; Mathematics; Demography","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":[],"consensus_categories":[],"category_scores_codex":[0.0007965519,0.00008983314,0.0001808494,0.0009384119,0.0004180669,0.001422358,0.0002457584,0.0003425838,0.007256585],"category_scores_gemma":[0.0140078,0.00009474299,0.0001639389,0.001108511,0.0005174562,0.0005805049,0.0005178909,0.0007284613,0.0007507557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006486177,"about_ca_system_score_gemma":0.0005685087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004576623,"about_ca_topic_score_gemma":0.00545291,"domain_scores_codex":[0.9992225,0.0001983406,0.00006379648,0.00006699958,0.0001982128,0.0002500217],"domain_scores_gemma":[0.9837542,0.009344856,0.003864963,0.0004624721,0.0009026879,0.001670855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008038381,0.0002014745,0.9657078,0.00002942033,0.00001741324,0.0001623502,0.0005325507,0.00230883,0.0004048902,0.006017141,0.0005880675,0.02394968],"study_design_scores_gemma":[0.00000587946,0.00006077325,0.9917306,0.00001891141,0.000008769297,0.0000883353,0.0007648906,0.003157966,0.0002966466,0.002545044,0.001316121,0.000006084931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916664,0.0001740076,0.0009053867,0.0005774403,0.00001015994,0.0000107661,0.00009650253,0.000007867992,0.006551498],"genre_scores_gemma":[0.9989492,0.00004484857,0.00007975291,0.00001189639,0.000006964673,0.000002679863,0.00003998363,0.00000100884,0.0008638693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007256585,"threshold_uncertainty_score":0.02427572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009593899347895354,"score_gpt":0.2683094005987837,"score_spread":0.2587155012508884,"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."}}