{"id":"W3195670816","doi":"10.1002/asi.24569","title":"Measuring the citation context of national <scp>self‐references</scp>","year":2021,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Scrutiny; Context (archaeology); Citation; Scholarly communication; Political science; Library science; Social science; Sociology; Public relations; Computer science; History; Publishing; Law","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"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.03892127,0.00005531783,0.0001693659,0.01278199,0.000546717,0.0009297582,0.001588762,0.0001021346,0.000003502895],"category_scores_gemma":[0.2719673,0.00002865544,0.00008782309,0.06951667,0.0002489846,0.002619456,0.0002940921,0.0002170153,0.00000775571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178072,"about_ca_system_score_gemma":0.00137757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002120042,"about_ca_topic_score_gemma":0.000004764243,"domain_scores_codex":[0.9903669,0.00008498348,0.0008921085,0.0001058905,0.008336905,0.0002131697],"domain_scores_gemma":[0.9480344,0.004331439,0.002372103,0.0002121021,0.0449886,0.00006133491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001340641,0.0001595105,0.3534548,0.00002457869,0.0001299419,4.52765e-7,0.0056598,0.0002629408,0.01054979,0.3357051,0.04345887,0.2505808],"study_design_scores_gemma":[0.001788276,0.000278937,0.3221318,0.00003742174,0.00003302712,0.00008479811,0.0291229,0.007692167,0.09408247,0.1965816,0.3480657,0.0001008591],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692059,0.0003002198,0.002265156,0.01833773,0.001204524,0.0002750258,0.00003447525,0.00001240013,0.008364565],"genre_scores_gemma":[0.9989198,0.0000906934,0.0004769779,0.0002587219,0.00002503588,0.000003757702,5.073799e-7,0.000001194713,0.0002232539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3046069,"threshold_uncertainty_score":0.9984073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3249558079521777,"score_gpt":0.4635263215457557,"score_spread":0.138570513593578,"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."}}