{"id":"W2964187120","doi":"10.7910/dvn/k7aw6f","title":"Fashion conversation data on Instagram","year":2017,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Fashion and Cultural Textiles","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Impromptu; Conversation; Product (mathematics); Computer science; Social media; Convolutional neural network; Fashion industry; Advertising; World Wide Web; Artificial intelligence; Clothing; Psychology; Communication; Business","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002179898,0.0003745207,0.0003393955,0.0001180813,0.0006495088,0.00107516,0.002225842,0.0002000135,0.1167694],"category_scores_gemma":[0.0002390298,0.0002985256,0.00008579985,0.00001311288,0.0003269709,0.001091784,0.0007367593,0.0004558007,0.3587782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005424333,"about_ca_system_score_gemma":0.00007577075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004378752,"about_ca_topic_score_gemma":0.005835939,"domain_scores_codex":[0.9982548,0.00007317851,0.000289595,0.0006665417,0.0004383268,0.0002774984],"domain_scores_gemma":[0.995528,0.00004783684,0.0003800612,0.003842445,0.00008789829,0.0001137509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002485924,0.00008437555,2.791594e-7,0.00008230954,0.00006171266,0.00003518761,0.0008301826,2.490628e-7,9.889563e-7,0.003235851,0.9945559,0.001088107],"study_design_scores_gemma":[0.0004112588,0.00007378518,0.000007390343,0.0001308954,0.0001198685,0.000002474093,0.002702453,0.00002018613,8.109118e-7,0.00001867701,0.9961116,0.0004006558],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005663983,3.637169e-7,4.76428e-7,0.0000415898,0.002721701,0.000285283,0.9864407,0.00009012472,0.01036317],"genre_scores_gemma":[0.0003537129,0.000299567,0.000006618167,0.0008945041,0.00133618,0.0000123382,0.9883699,0.00002105451,0.008706104],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2420088,"threshold_uncertainty_score":0.9999618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0903461211020638,"score_gpt":0.2905872352522844,"score_spread":0.2002411141502206,"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."}}