{"id":"W2059064846","doi":"10.1016/s0953-5438(02)00063-2","title":"What is this evasive beast we call user satisfaction?","year":2003,"lang":"en","type":"article","venue":"Interacting with Computers","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":320,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Usability; Computer science; Construct (python library); Exploratory research; Human–computer interaction; Psychology; User experience design; Applied psychology; Computer user satisfaction; Appeal; Usability lab; User interface; USable; Social psychology; World Wide Web; Usability engineering; User interface design","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002818196,0.0003721015,0.0003074136,0.0004568802,0.0003271054,0.0008651088,0.0007438426,0.0001421085,0.0001666944],"category_scores_gemma":[0.0001621777,0.0003349123,0.00009470916,0.0007252361,0.0001137325,0.00443504,0.0002133674,0.0008269573,0.0004559963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003390823,"about_ca_system_score_gemma":0.00009805585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008270997,"about_ca_topic_score_gemma":0.0000622807,"domain_scores_codex":[0.9976155,0.0001723753,0.0004500138,0.000833231,0.0004181855,0.0005107238],"domain_scores_gemma":[0.9972016,0.0008735873,0.0004635945,0.0008439688,0.0005305925,0.00008663187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003488876,0.0008928257,0.05846076,0.0002175044,0.002409478,0.001231313,0.07099754,0.00202104,0.01255226,0.1859508,0.2817011,0.3832165],"study_design_scores_gemma":[0.0073331,0.003594063,0.01762382,0.01128257,0.0001922904,0.01083025,0.01051657,0.08026899,0.2244617,0.007143301,0.6206312,0.006122183],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1815982,0.0001407658,0.7788094,0.01080424,0.02309698,0.0006077341,0.000002529069,0.001097614,0.003842504],"genre_scores_gemma":[0.915352,0.00002787809,0.08056107,0.002655957,0.0001015548,0.00003528443,0.00000224772,0.00003853054,0.001225506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7337538,"threshold_uncertainty_score":0.9999103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531716116989479,"score_gpt":0.2723196743358931,"score_spread":0.2570025131659983,"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."}}