{"id":"W1498564171","doi":"10.18438/b8kg8w","title":"Development of Technology Competencies for Public Services’ Staff Has Limited External Validity","year":2011,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Desk; Staffing; Service desk; Reference desk; House of Commons; Computer science; World Wide Web; Documentation; Public relations; Business; Political science; Marketing; Law","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2610138,0.001432798,0.002274474,0.008782471,0.005378577,0.006970017,0.003670569,0.002342971,0.01012189],"category_scores_gemma":[0.5187172,0.001505108,0.003932722,0.01045371,0.01188464,0.009288,0.01078788,0.002861148,0.003101109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007527049,"about_ca_system_score_gemma":0.01328523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007187793,"about_ca_topic_score_gemma":0.008203771,"domain_scores_codex":[0.6612275,0.1988393,0.04662805,0.02263724,0.06370157,0.006966365],"domain_scores_gemma":[0.4059218,0.3878194,0.03672462,0.07729036,0.08862819,0.003615683],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002010681,0.001547285,0.4853078,0.01331902,0.00168671,0.0003808113,0.05059696,0.002380762,0.001145294,0.04296299,0.04631268,0.352349],"study_design_scores_gemma":[0.001625458,0.00287073,0.4738278,0.01735027,0.001330811,0.0005919953,0.06684851,0.0161301,0.01015741,0.07193105,0.3367592,0.0005766785],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.601936,0.005130368,0.1212016,0.01342274,0.003329057,0.05391743,0.01075467,0.0009002991,0.1894078],"genre_scores_gemma":[0.8929551,0.0006771688,0.04007921,0.002973857,0.0002781686,0.05541525,0.002720633,0.0003392408,0.004561331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7389862,"threshold_uncertainty_score":0.9113016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08836228756882947,"score_gpt":0.2889454030759793,"score_spread":0.2005831155071499,"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."}}