{"id":"W4413164636","doi":"10.2139/ssrn.5373331","title":"Learning From the Best: What Makes Popular Hugging Face Models? A Registered Report","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Face (sociological concept); Psychology; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"evaluation","study_design":"observational","genre":"protocol","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"protocol","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002645469,0.0003984854,0.000528765,0.0002388824,0.0006764197,0.002393074,0.002246724,0.0002571681,0.00001180125],"category_scores_gemma":[0.0002016264,0.000308957,0.0006339484,0.0003940708,0.00004707563,0.0009591665,0.001110744,0.007314296,0.00002622512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000867309,"about_ca_system_score_gemma":0.003195181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005360615,"about_ca_topic_score_gemma":0.0006272519,"domain_scores_codex":[0.9954188,0.0004742212,0.0007495221,0.0008600468,0.000702463,0.001794911],"domain_scores_gemma":[0.9974434,0.0001163262,0.0009378672,0.001126326,0.0002655647,0.000110474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003174484,0.00016381,0.0007368486,0.00006031069,0.003003499,0.0004370242,0.004564641,0.1823135,0.00008601444,0.05303405,0.0004190782,0.7551494],"study_design_scores_gemma":[0.0003427743,0.00006486772,0.00001293079,0.001159488,0.0003072458,0.0009742187,0.01047697,0.3414763,0.00003858004,0.6420121,0.002584579,0.0005499132],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01211991,0.02761709,0.9458795,0.01224558,0.0009163179,0.0002214054,0.000003607409,0.0001743807,0.0008222263],"genre_scores_gemma":[0.8462213,0.1132725,0.006183833,0.00116968,0.0008723542,0.00005279202,0.0001379965,0.00006066661,0.03202893],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9396957,"threshold_uncertainty_score":0.9999362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522229291286052,"score_gpt":0.2734117608359626,"score_spread":0.2381894679231021,"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."}}