{"id":"W3002419061","doi":"10.24908/pceea.vi0.13830","title":"WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"University of Toronto","keywords":"Wonder; Subject (documents); Identity (music); Focus (optics); Object (grammar); Point (geometry); Data science; Natural (archaeology); Computer science; Macro; Artificial intelligence; Epistemology; Sociology; Linguistics; Natural language processing; Cognitive science; Psychology; History; World Wide Web; Aesthetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219244,0.0001125079,0.0002754305,0.0004669199,0.0001643353,0.0001453665,0.0001692363,0.00008019932,0.00002754136],"category_scores_gemma":[0.0006170109,0.000111907,0.00006567661,0.001071255,0.00003857879,0.0006307505,0.00003948176,0.0002037485,4.458494e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071728,"about_ca_system_score_gemma":0.0003089385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009231165,"about_ca_topic_score_gemma":0.01174075,"domain_scores_codex":[0.9988817,0.00001639161,0.0003090535,0.000195153,0.0004034257,0.0001942657],"domain_scores_gemma":[0.9987727,0.00005649151,0.0005189556,0.00003254987,0.0005173866,0.0001018865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007686596,0.00007239552,0.8989648,0.005623646,0.0001341037,1.049569e-7,0.04455332,0.0002447242,0.001599472,0.02123656,0.001797238,0.02576592],"study_design_scores_gemma":[0.0003818582,0.00001436939,0.9398419,0.0113911,0.0001671133,0.000006197391,0.01697216,0.02508961,0.0002051162,0.001007347,0.004461349,0.0004618945],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833364,0.00921818,0.000009360961,0.004827878,0.0003810886,0.000222645,0.000004447887,0.00002168688,0.001978339],"genre_scores_gemma":[0.9976733,0.0004551376,0.0009116363,0.00008559986,0.0001010438,0.000004891261,0.000002157598,0.00001087585,0.0007554121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04087706,"threshold_uncertainty_score":0.9973664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440868493388743,"score_gpt":0.3079810344921675,"score_spread":0.2935723495582801,"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."}}