{"id":"W2897372017","doi":"10.26481/dis.20180926ah","title":"Counting for EU enlargement?","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"European Union Policy and Governance","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Council on Foreign Relations","funders":"","keywords":"Census; Resizing; Member states; Geography; Population; Political science; Politics; Eu countries; Variation (astronomy); Order (exchange); Regional science; European union; Demography; Law; International trade; Business; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004698198,0.00008281916,0.00009386707,0.00002856951,0.0004510712,0.00007339569,0.000212071,0.0001020995,0.0009070534],"category_scores_gemma":[0.0003907105,0.00008186974,0.00006608148,0.00007886175,0.00003812682,0.0000870579,0.000006176519,0.00005686022,0.0002305901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003334397,"about_ca_system_score_gemma":0.0001377424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328506,"about_ca_topic_score_gemma":0.01429808,"domain_scores_codex":[0.999222,0.00003162341,0.0001355012,0.0001645954,0.0002218021,0.0002244152],"domain_scores_gemma":[0.9994762,0.00005954802,0.0001669931,0.00009010066,0.0001667927,0.00004034271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001433423,0.00001201162,0.00002328628,0.00003886349,0.0000132967,2.876698e-7,0.006668308,2.663189e-8,0.00001894318,0.7463042,0.243935,0.002971388],"study_design_scores_gemma":[0.00008111093,0.00001348919,0.0004709848,0.00003765243,0.00001091595,1.380137e-8,0.002816564,0.000001696494,0.0002415822,0.001304088,0.9949102,0.0001117073],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00334649,0.00006502633,0.0001095845,0.0001899039,0.001198046,0.0002365447,0.00002292099,0.00006046673,0.994771],"genre_scores_gemma":[0.02448927,0.0001672847,0.0008023151,0.001443877,0.001902856,0.00002011306,0.0001652296,0.00002332619,0.9709857],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7509752,"threshold_uncertainty_score":0.9931598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03143514503231093,"score_gpt":0.3626663410012976,"score_spread":0.3312311959689867,"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."}}