{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.05035313,0.00008141617,0.0002726399,0.02363999,0.0006604472,0.001330229,0.003156022,0.0006140887,0.000002595929],"category_scores_gemma":[0.2993588,0.00004311146,0.0001362294,0.06731717,0.0006459874,0.002063113,0.001536077,0.001434784,0.000002022499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007508069,"about_ca_system_score_gemma":0.002830283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001229748,"about_ca_topic_score_gemma":0.00003612735,"domain_scores_codex":[0.9911141,0.00009704022,0.001553117,0.0001532015,0.006804504,0.0002780373],"domain_scores_gemma":[0.972359,0.002998614,0.003816459,0.0003932067,0.02036346,0.00006928097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004275935,0.0001048358,0.7405068,0.00003112297,0.00006061724,4.845526e-7,0.001375812,0.005232483,0.000971898,0.04595561,0.0096826,0.196035],"study_design_scores_gemma":[0.001756989,0.000133592,0.6224186,0.0001315606,0.0000310362,0.00002613471,0.006514255,0.05330025,0.006838661,0.08111022,0.2275064,0.0002322805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546266,0.000827258,0.00197903,0.0389619,0.002153124,0.0005021435,0.00007392707,0.000006497124,0.0008694823],"genre_scores_gemma":[0.9985367,0.0005565044,0.0006787104,0.00006553896,0.00002541523,0.00001187705,0.000001041089,0.000001400361,0.000122818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2490057,"threshold_uncertainty_score":0.9997065,"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."}}