{"id":"W4210354806","doi":"10.1101/2022.01.21.477291","title":"Becoming metrics literate: An analysis of brief videos that teach about the h-index","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Uniformed Services University of the Health Sciences; Social Sciences and Humanities Research Council of Canada; U.S. Department of Defense","keywords":"Index (typography); Coding (social sciences); Computer science; Quality (philosophy); Cognitive load; Content analysis; Presentation (obstetrics); Promotion (chess); Multimedia; Cognition; Psychology; World Wide Web; Sociology","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005769066,0.0003316348,0.0002319658,0.003711287,0.0009261108,0.001923346,0.000447991,0.0006291041,0.003034758],"category_scores_gemma":[0.0624409,0.0001675634,0.000247484,0.002074414,0.0008343136,0.001845921,0.001094486,0.0007852947,0.0005420726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002056301,"about_ca_system_score_gemma":0.001451297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00490501,"about_ca_topic_score_gemma":0.01057922,"domain_scores_codex":[0.997279,0.001422966,0.0001947853,0.0001824472,0.0006825896,0.0002382239],"domain_scores_gemma":[0.9382646,0.04675182,0.005087851,0.0008799787,0.007565372,0.001450356],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001022944,0.0009851899,0.1616038,0.004590078,0.00009809195,0.003134388,0.3013091,0.0006424548,0.0114001,0.00320224,0.03489866,0.4771129],"study_design_scores_gemma":[0.00006893274,0.001291608,0.5816324,0.005350315,0.0001269067,0.002110285,0.2486947,0.004042967,0.006607195,0.00251813,0.1473073,0.0002493032],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712683,0.0008440677,0.006498192,0.001303261,0.0001859123,0.00147252,0.002281093,0.0001342951,0.01601225],"genre_scores_gemma":[0.9676937,0.001626369,0.0191038,0.0008390211,0.0001961961,0.002108347,0.002368495,0.0001503963,0.005913607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9962887,"threshold_uncertainty_score":0.03051007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2688159331280403,"score_gpt":0.4461677401245696,"score_spread":0.1773518069965293,"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."}}