{"id":"W4214600529","doi":"10.1108/jsm-09-2021-0363","title":"Human enhancement technologies and the future of consumer well-being","year":2022,"lang":"en","type":"article","venue":"Journal of Services Marketing","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Transformative learning; Transhumanism; Originality; Services marketing; Marketing; Value (mathematics); Work (physics); Service (business); Conceptual framework; Service-dominant logic; Business; Sociology; Knowledge management; Engineering; Computer science; Social science; Qualitative research","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":[],"consensus_categories":[],"category_scores_codex":[0.002998105,0.0001082823,0.0002305171,0.0002013505,0.0006524269,0.00004774572,0.0006409839,0.00003551166,0.0002335691],"category_scores_gemma":[0.0002984634,0.00007613956,0.00005852024,0.0004744727,0.0004570975,0.0001506833,0.000582987,0.0007712378,9.202348e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003487504,"about_ca_system_score_gemma":0.00002785075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004693068,"about_ca_topic_score_gemma":0.00000402909,"domain_scores_codex":[0.9978181,0.0004727009,0.0006893771,0.0001699639,0.0006669124,0.0001829614],"domain_scores_gemma":[0.9976879,0.0007768084,0.001197074,0.0001937958,0.0001197365,0.00002463986],"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":[0.00044061,0.0002057921,0.001468294,0.0005416667,0.00006209279,0.0000502449,0.002461095,0.00001999907,0.9587586,0.01682786,0.0009082579,0.01825551],"study_design_scores_gemma":[0.009073704,0.001143894,0.004782626,0.001116257,0.0002815982,0.0006244002,0.07718682,0.001133634,0.6566973,0.0544342,0.1926983,0.0008272334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885316,0.0008992557,0.00003607969,0.007412867,0.0005188184,0.0001655368,0.000002482739,0.00002562023,0.002407738],"genre_scores_gemma":[0.9970432,0.0005446228,0.0002528935,0.00192338,0.0000940884,0.000009410848,3.111522e-7,0.00001064969,0.000121457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3020613,"threshold_uncertainty_score":0.5018005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439136266185699,"score_gpt":0.278693168159586,"score_spread":0.264301805497729,"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."}}