{"id":"W4407722685","doi":"10.1007/s12134-025-01239-z","title":"Digital Supports for Immigrant Professionals’ Settlement and Information Needs: Developing a Wiki-Style Tool with Intersectional, Targeted Content","year":2025,"lang":"en","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Immigration, Refugees and Citizenship Canada","keywords":"Immigration; Style (visual arts); Settlement (finance); Digital content; World Wide Web; Sociology; Political science; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00436491,0.0007797877,0.0004675394,0.003355169,0.001940969,0.005348911,0.001696617,0.001281665,0.006392536],"category_scores_gemma":[0.01557668,0.0003988968,0.0004841429,0.00192614,0.0007481826,0.0085965,0.008295995,0.001152309,0.001742062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000521314,"about_ca_system_score_gemma":0.002463422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008460202,"about_ca_topic_score_gemma":0.002531808,"domain_scores_codex":[0.9985042,0.0006765097,0.0002295242,0.0001869966,0.0003058909,0.00009694478],"domain_scores_gemma":[0.9860662,0.009352438,0.0006791774,0.001388969,0.001140079,0.001373146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000428204,0.002310799,0.01743216,0.001688009,0.000095682,0.001177977,0.08305266,0.001346354,0.01278485,0.01550658,0.03093796,0.8332388],"study_design_scores_gemma":[0.0004941925,0.001489395,0.03043045,0.003740919,0.0006179887,0.003720019,0.1599728,0.05232413,0.03463604,0.04864413,0.6633084,0.0006214692],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5700933,0.0006761987,0.299668,0.005379158,0.0006395152,0.002365154,0.002324973,0.03069697,0.08815683],"genre_scores_gemma":[0.4900609,0.0005117559,0.4882088,0.000498521,0.00008849938,0.001676093,0.002119895,0.001071443,0.01576403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006392536,"threshold_uncertainty_score":0.0230841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01377854669965113,"score_gpt":0.3132830897790403,"score_spread":0.2995045430793892,"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."}}