{"id":"W4412560899","doi":"10.31219/osf.io/5wuyn_v2","title":"Now is the Time: Operationalizing Generative Neurophenomenology through Interpersonal Methods","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neuroscience, Education and Cognitive Function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Usona Institute; CHU Sainte-Justine Foundation; McGill University","keywords":"Operationalization; Generative grammar; Interpersonal communication; Psychology; Computer science; Social psychology; Artificial intelligence; Epistemology; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003807753,0.0003606817,0.0002919389,0.0001496755,0.0006967725,0.000379852,0.0008613663,0.0001565227,0.003413798],"category_scores_gemma":[0.0009751202,0.0002549927,0.0001713374,0.0005252049,0.0005014801,0.0002158401,0.00101013,0.0009030253,0.0004533131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014567,"about_ca_system_score_gemma":0.0006338748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001781368,"about_ca_topic_score_gemma":0.000003069439,"domain_scores_codex":[0.996046,0.001553213,0.0003908353,0.001328151,0.0003509667,0.0003308284],"domain_scores_gemma":[0.997959,0.001033485,0.0001933032,0.0005428998,0.0002135707,0.00005771587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008765308,0.0004236487,0.00004357018,0.00006240457,0.00005288245,0.00001018506,0.008978333,0.0006123394,0.7297328,0.1498736,0.09436382,0.01575876],"study_design_scores_gemma":[0.0003229183,0.0001471312,0.0001561243,0.00006343909,0.00007553622,0.00004712363,0.0006307021,0.03168644,0.6319923,0.02528321,0.3089329,0.0006621931],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0133834,0.0002057179,0.3854407,0.09765648,0.01756866,0.002198233,0.0003266958,0.000454284,0.4827659],"genre_scores_gemma":[0.04852491,0.0007293406,0.03810145,0.4433855,0.001343299,0.001041337,0.00006662706,0.00007457374,0.4667329],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3473392,"threshold_uncertainty_score":0.9999902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09575971435498307,"score_gpt":0.3956606397796569,"score_spread":0.2999009254246738,"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."}}