{"id":"W4403439557","doi":"10.1007/978-3-031-62170-3_6","title":"Use of Natural Language Processing (NLP) to Support Assuring the Internal Validity of Qualitative Research","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Natural language processing; Computer science; Artificial intelligence; Internal validity; Linguistics; Philosophy; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1862872,0.001030938,0.001155524,0.006582546,0.003313238,0.007482883,0.002686138,0.001198243,0.01347376],"category_scores_gemma":[0.389522,0.002029853,0.001211676,0.005905479,0.005484523,0.007872638,0.009673957,0.003763833,0.002715413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003839771,"about_ca_system_score_gemma":0.00823482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002969845,"about_ca_topic_score_gemma":0.008466332,"domain_scores_codex":[0.8296852,0.1395737,0.01320358,0.007743434,0.008933026,0.0008610739],"domain_scores_gemma":[0.2298028,0.7087008,0.01156351,0.03233822,0.01635749,0.001237091],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004212314,0.0003253059,0.004412436,0.004615624,0.0002224682,0.0007907102,0.1765269,0.002458573,0.02029252,0.1230962,0.03659276,0.6302454],"study_design_scores_gemma":[0.0006369157,0.0004332736,0.01674703,0.006043628,0.0003469728,0.002608156,0.0619549,0.1301453,0.05979088,0.4595436,0.2611379,0.0006114163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01193337,0.0001981605,0.9506134,0.002729111,0.0002091236,0.004678567,0.001358255,0.006516167,0.02176388],"genre_scores_gemma":[0.03079673,0.00007711368,0.9591192,0.0003453047,0.00002841371,0.006703602,0.0004077858,0.000754462,0.001767376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8137128,"threshold_uncertainty_score":0.9851927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1850091016747154,"score_gpt":0.475654709049408,"score_spread":0.2906456073746926,"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."}}