{"id":"W1942393901","doi":"10.1002/asi.23107","title":"In‐text author citation analysis: Feasibility, benefits, and limitations","year":2014,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Ranking (information retrieval); Computer science; Citation analysis; Information retrieval; Data science; World Wide Web","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.1171845,0.001448528,0.001615971,0.01164905,0.00198412,0.009766973,0.003679535,0.001772955,0.004553273],"category_scores_gemma":[0.3710494,0.0005494289,0.001385684,0.02046193,0.002331908,0.01350294,0.002581486,0.001773966,0.002284253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002094305,"about_ca_system_score_gemma":0.003472126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005006257,"about_ca_topic_score_gemma":0.00418377,"domain_scores_codex":[0.8667386,0.09548222,0.007309536,0.004028744,0.02564845,0.0007924825],"domain_scores_gemma":[0.3944707,0.4569165,0.02245027,0.03279872,0.09120528,0.002158656],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009595688,0.0007381998,0.1158317,0.006266391,0.001243442,0.0003557495,0.005281198,0.003854915,0.002749205,0.03815378,0.02634036,0.7982255],"study_design_scores_gemma":[0.0005011957,0.001552699,0.1499352,0.008855816,0.003167361,0.004676857,0.01680133,0.1857513,0.0272581,0.31423,0.2863325,0.0009376244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2375601,0.03247898,0.5833763,0.05032092,0.007890168,0.002545094,0.007497517,0.003144037,0.07518684],"genre_scores_gemma":[0.6698759,0.007210121,0.3068295,0.003474874,0.002832937,0.00152017,0.002334868,0.0006720886,0.005249558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9883509,"threshold_uncertainty_score":0.6197385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.34927831880499,"score_gpt":0.5014277466597552,"score_spread":0.1521494278547652,"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."}}