{"id":"W4392564714","doi":"10.32866/001c.94401","title":"A Comparison of the Results from Artificial Intelligence-based and Human-based Transport-related Thematic Analysis","year":2024,"lang":"en","type":"article","venue":"Findings","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Thematic analysis; Thematic map; Computer science; Field (mathematics); Human intelligence; Process (computing); Data science; Subject (documents); Artificial intelligence; Psychology; Qualitative research; World Wide Web; Sociology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04432864,0.0006783576,0.000704366,0.007570453,0.001374692,0.004224515,0.001115742,0.0008437274,0.008558582],"category_scores_gemma":[0.1818382,0.0002079025,0.001383563,0.006219208,0.001624417,0.004980209,0.003845913,0.001002355,0.002746662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002156114,"about_ca_system_score_gemma":0.002467739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005038125,"about_ca_topic_score_gemma":0.009181224,"domain_scores_codex":[0.9692214,0.01953533,0.003020418,0.002265276,0.005033378,0.0009241492],"domain_scores_gemma":[0.8193527,0.1264815,0.009399748,0.01217966,0.03116345,0.001422989],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002640228,0.0006385901,0.2832919,0.01346373,0.001706765,0.0005513992,0.1503207,0.004018447,0.01749874,0.02195448,0.04435625,0.4595588],"study_design_scores_gemma":[0.0001709557,0.000735901,0.5003207,0.00455902,0.001012567,0.000770509,0.233376,0.01860049,0.02168011,0.05765642,0.1605558,0.0005615975],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7055568,0.0021108,0.1796248,0.00449665,0.0009414924,0.002492715,0.03036541,0.001923653,0.07248773],"genre_scores_gemma":[0.8791341,0.0007703811,0.08983033,0.0007909724,0.000156385,0.002690675,0.01891068,0.000946419,0.006770009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9556714,"threshold_uncertainty_score":0.2344351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2083072334557215,"score_gpt":0.447583453892382,"score_spread":0.2392762204366605,"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."}}