{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.0455198,0.0000541337,0.0001988511,0.04856048,0.0003770715,0.001116068,0.0009298666,0.0001085515,0.000002585619],"category_scores_gemma":[0.2199398,0.00003308753,0.00006698106,0.1340582,0.000211202,0.003895314,0.0002192995,0.0001705067,0.000004775718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000260007,"about_ca_system_score_gemma":0.0002055877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000418162,"about_ca_topic_score_gemma":0.00003583827,"domain_scores_codex":[0.9951604,0.00007420545,0.0008162161,0.0001336137,0.003599985,0.0002155988],"domain_scores_gemma":[0.9849486,0.003163458,0.001417563,0.0002289055,0.01015109,0.00009038266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001085028,0.00003076445,0.6664028,0.000003199999,0.00002651407,3.290914e-8,0.0006321903,0.0003854755,0.0001837693,0.1068413,0.001665303,0.2238178],"study_design_scores_gemma":[0.0005890708,0.0001473736,0.8809168,0.000005197267,0.00003589592,0.000005217775,0.001305892,0.01920825,0.0003334375,0.07215886,0.02522685,0.00006714942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571044,0.000113856,0.00390658,0.03673122,0.0004363596,0.0002505513,0.0000101552,0.000007102617,0.001439729],"genre_scores_gemma":[0.9987292,0.00008276395,0.0008387695,0.0002231703,0.00001405093,0.000003881248,3.908771e-7,9.851217e-7,0.0001067568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2237506,"threshold_uncertainty_score":0.9999208,"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."}}