{"id":"W3088110085","doi":"10.1002/asi.24472","title":"The rise of multiple institutional affiliations in academia","year":2021,"lang":"en","type":"preprint","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; Deutsche Forschungsgemeinschaft","keywords":"Quarter (Canadian coin); Excellence; China; Political science; Regional science; Economic growth; Geography; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005898851,0.0001452927,0.0004272317,0.007194354,0.001692689,0.005366965,0.0007249156,0.0009081595,0.007625322],"category_scores_gemma":[0.04342689,0.000233332,0.0002392166,0.01478369,0.002232083,0.0048955,0.005016738,0.001133578,0.0006833269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001798622,"about_ca_system_score_gemma":0.001519028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00226437,"about_ca_topic_score_gemma":0.003462831,"domain_scores_codex":[0.9882879,0.00477509,0.001057193,0.001853648,0.002751144,0.001275109],"domain_scores_gemma":[0.8655508,0.05900942,0.04980779,0.005644751,0.008778853,0.01120836],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002313817,0.00007917836,0.8905907,0.0004331247,0.0002219723,0.0006777485,0.009222175,0.000984942,0.001203727,0.02804185,0.003535534,0.06477766],"study_design_scores_gemma":[0.0000361358,0.000141116,0.9096107,0.0006124415,0.0001492253,0.001019103,0.02528055,0.001316118,0.00156461,0.02279693,0.03739269,0.00008035832],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663377,0.005396443,0.002160141,0.004445835,0.0001658354,0.00001848031,0.0008394201,0.00004376593,0.02059227],"genre_scores_gemma":[0.9987318,0.0004247956,0.0002483927,0.00009235885,0.00005804703,0.000003621099,0.00008183516,0.000004213282,0.0003548325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9941012,"threshold_uncertainty_score":0.03119648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2331593821368919,"score_gpt":0.5033470671943766,"score_spread":0.2701876850574847,"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."}}