{"id":"W7018581092","doi":"","title":"The (in)efficiency of within-language variation in online communities","year":2024,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Variation (astronomy); Domain (mathematical analysis); Social media; Online community; Computer-mediated communication","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005635479,0.0001316714,0.0003071082,0.002218746,0.001318704,0.002208643,0.0005758088,0.000423135,0.001825849],"category_scores_gemma":[0.0311687,0.0002093283,0.0002147878,0.001786253,0.00221074,0.002133305,0.002218884,0.0004794789,0.0002590772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009287733,"about_ca_system_score_gemma":0.0006015429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006851733,"about_ca_topic_score_gemma":0.01440105,"domain_scores_codex":[0.9948959,0.003021115,0.000253263,0.0008829103,0.0005842374,0.0003626521],"domain_scores_gemma":[0.971534,0.01561179,0.005498969,0.003246394,0.002811455,0.001297533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002563749,0.0001823051,0.8676475,0.0001331334,0.0002124321,0.0003444479,0.06663697,0.001213619,0.008405686,0.005855475,0.0005846241,0.04852742],"study_design_scores_gemma":[0.00001640147,0.0001079361,0.9673352,0.00002751252,0.0000389188,0.0003828651,0.01955086,0.003725856,0.001310598,0.004344141,0.003103318,0.00005630181],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968553,0.00008253428,0.001178793,0.00007015189,0.000002667478,0.00001234096,0.00006657943,0.000009038034,0.001722639],"genre_scores_gemma":[0.999474,0.00001340899,0.0003062666,0.000007433797,0.000001758042,0.000005523502,0.0000308353,0.000006043997,0.0001548098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006851733,"threshold_uncertainty_score":0.02980363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451701642720122,"score_gpt":0.257147632811595,"score_spread":0.2426306163843938,"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."}}