{"id":"W2913788750","doi":"10.12948/issn14531305/22.4.2018.03","title":"Using Face Recognition with Twitter Data for the Study of International Migration","year":2018,"lang":"en","type":"article","venue":"Informatica Economica","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Electronic Arts (Canada)","funders":"","keywords":"Face (sociological concept); Facial recognition system; Computer science; World Wide Web; Internet privacy; Data science; Artificial intelligence; Pattern recognition (psychology); 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":[0.0006985582,0.00004143725,0.0000741081,0.00004668502,0.0003065951,0.00008200192,0.0003591113,0.00002157672,0.0002990575],"category_scores_gemma":[0.0001177824,0.00002997238,0.0000179754,0.00006358061,0.000171755,0.0005443246,0.00003321027,0.00002927657,0.00003082772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004686114,"about_ca_system_score_gemma":0.00009155195,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002604573,"about_ca_topic_score_gemma":0.02177934,"domain_scores_codex":[0.999467,0.00003167374,0.0002560168,0.00008228578,0.00008723561,0.00007574568],"domain_scores_gemma":[0.99919,0.0001961812,0.0001685349,0.0002809208,0.0001448802,0.00001949259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006132986,0.001150759,0.0376333,0.00009316252,0.001658565,1.647535e-7,0.7815015,0.00532525,0.0000601769,0.001675581,0.008256731,0.1620315],"study_design_scores_gemma":[0.001075245,0.0003167278,0.004044823,0.00003241641,0.0002625885,4.44064e-7,0.2578032,0.6712311,0.0001224728,0.0007243102,0.0641754,0.0002113294],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745234,0.00000174908,0.02095559,0.001013919,0.00008159191,0.0005178943,0.00003726455,0.00001071775,0.002857857],"genre_scores_gemma":[0.9986423,0.000003364735,0.0009177741,0.0001436715,0.0001597963,0.00002089624,0.00006474809,0.000002427021,0.00004501471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6659058,"threshold_uncertainty_score":0.9960706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1705976389770633,"score_gpt":0.3709333783041573,"score_spread":0.2003357393270941,"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."}}