{"id":"W4400796313","doi":"10.1093/oso/9780197655658.003.0011","title":"Migrants Become Immigrants","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Geography; Political science; Demographic economics; Economics; Archaeology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005291319,0.0003464961,0.0001857089,0.0005854146,0.004718761,0.004507135,0.0004978183,0.001338301,0.01825326],"category_scores_gemma":[0.0007360297,0.000178895,0.0002094485,0.0005789856,0.002158738,0.002830567,0.002728395,0.001693458,0.003514443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536583,"about_ca_system_score_gemma":0.002359378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00600819,"about_ca_topic_score_gemma":0.0134321,"domain_scores_codex":[0.9996307,0.0001768912,0.00000946075,0.00003666642,0.00005210832,0.0000942052],"domain_scores_gemma":[0.9998017,0.00005200265,0.00002203422,0.00001285551,0.0000429826,0.00006839219],"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":[0.00004122927,0.0000983754,0.003866355,0.0002348724,0.000005212552,0.002267475,0.115849,0.0001127102,0.0003604828,0.5222238,0.2709965,0.08394391],"study_design_scores_gemma":[0.000005664425,0.00002394418,0.001067856,0.0002470367,0.000002595154,0.0003324784,0.03776367,0.00002683224,0.00005696748,0.007123959,0.9533439,0.000005175748],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.06097274,0.01321383,0.0004248954,0.02796822,0.00444252,0.00006180072,0.0001144927,0.00004880203,0.8927527],"genre_scores_gemma":[0.1533091,0.01151671,0.0003903616,0.007696706,0.001060012,0.00008881914,0.0000884781,0.00004744146,0.8258023],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01825326,"threshold_uncertainty_score":0.06106329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121708263134154,"score_gpt":0.2902049990857707,"score_spread":0.2689879164544292,"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."}}