{"id":"W4409639126","doi":"10.1007/s11135-025-02137-3","title":"Revisiting acculturation research with big data: the case of the Italian diaspora through the lens of Facebook interests","year":2025,"lang":"en","type":"article","venue":"Quality & Quantity","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"European University Institute","keywords":"Diaspora; Acculturation; Through-the-lens metering; Big data; Sociology; Lens (geology); Social media; Psychology; Internet privacy; Media studies; Political science; Gender studies; Computer science; World Wide Web; Anthropology; Ethnic group; Data mining; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01735482,0.0005745937,0.000660664,0.006464571,0.003520876,0.01193292,0.001803642,0.001851026,0.002163337],"category_scores_gemma":[0.04775053,0.0004494221,0.0007515921,0.01110944,0.008966855,0.01124367,0.005996503,0.00376779,0.0003408423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003096451,"about_ca_system_score_gemma":0.002512152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02529212,"about_ca_topic_score_gemma":0.02725316,"domain_scores_codex":[0.988838,0.008526392,0.0003102942,0.0009030437,0.0009119662,0.0005104988],"domain_scores_gemma":[0.9257901,0.05748128,0.00583938,0.006982756,0.002481072,0.001425418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001278112,0.00015041,0.4924726,0.0009148594,0.0004406984,0.002707992,0.1338769,0.00236582,0.0004014706,0.2733913,0.01800748,0.07514279],"study_design_scores_gemma":[0.00003451317,0.00008387728,0.3090901,0.004068789,0.0002701206,0.001582505,0.2347503,0.0243279,0.0009642949,0.2311053,0.1935727,0.0001496825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7854224,0.01996171,0.03268825,0.09635892,0.0006506559,0.0001740144,0.004788314,0.0001380469,0.05981765],"genre_scores_gemma":[0.9828963,0.003099666,0.009372659,0.002323291,0.0004727409,0.00008388943,0.0008395126,0.00006329438,0.0008485506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02529212,"threshold_uncertainty_score":0.09178215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4466361616556275,"score_gpt":0.4965584913688289,"score_spread":0.04992232971320137,"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."}}