{"id":"W3212729498","doi":"10.5267/j.ijdns.2021.9.007","title":"Gender and age in the language of social media: An easier way to build credibility","year":2021,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Islamic Finance and Communication","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Credibility; Advertising; Product (mathematics); Nonprobability sampling; Business; Marketing; Social media; Source credibility; Sample (material); Position (finance); Sociology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.004602687,0.0005542926,0.0003855719,0.002070687,0.001605094,0.003588466,0.0003760653,0.0008557559,0.01561849],"category_scores_gemma":[0.01914396,0.0002367154,0.0003235944,0.001177937,0.00181254,0.00721075,0.00217652,0.001381922,0.002252256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007341428,"about_ca_system_score_gemma":0.0007535234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189033,"about_ca_topic_score_gemma":0.002404011,"domain_scores_codex":[0.9961211,0.002428059,0.0002527453,0.0003077309,0.0007029093,0.0001875412],"domain_scores_gemma":[0.9879243,0.007702595,0.001447893,0.0007789553,0.001699813,0.0004464289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001204202,0.0003820802,0.1134693,0.001536851,0.0001110875,0.001980072,0.2486098,0.0003212719,0.02109595,0.1178002,0.02437118,0.469118],"study_design_scores_gemma":[0.0001143746,0.0009574105,0.1646002,0.003221505,0.0004071273,0.003877949,0.1591602,0.003848512,0.01643153,0.06314865,0.5837408,0.0004916764],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5952327,0.006279258,0.07864475,0.02811975,0.005188068,0.0005776201,0.002005525,0.0007827174,0.2831696],"genre_scores_gemma":[0.9544715,0.001538469,0.0196321,0.001938045,0.000893678,0.0002188283,0.0002499347,0.0002309751,0.02082652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01561849,"threshold_uncertainty_score":0.05224907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06916076302333601,"score_gpt":0.3905968645773793,"score_spread":0.3214361015540433,"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."}}