{"id":"W2790155487","doi":"10.1177/1356766718757789","title":"Luxurious or economical? An identification of tourists’ preferred hotel attributes using best–worst scaling (BWS)","year":2018,"lang":"en","type":"article","venue":"Journal Of Vacation Marketing","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Saint Vincent University","funders":"","keywords":"Selection (genetic algorithm); Identification (biology); Marketing; Business; Hotel industry; Advertising; Task (project management); Balance (ability); Consumer behaviour; Multidimensional scaling; Tourism; Economics; Computer science; Psychology; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0007143343,0.0001578063,0.0001665945,0.000814414,0.0003768338,0.0007164123,0.0001100004,0.0001703925,0.001983501],"category_scores_gemma":[0.003099853,0.0000872052,0.000335419,0.0006245813,0.0004206382,0.000665959,0.0004946296,0.0003203126,0.0002644664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786218,"about_ca_system_score_gemma":0.0001914527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856592,"about_ca_topic_score_gemma":0.003664842,"domain_scores_codex":[0.999743,0.0000909857,0.000032726,0.0000210129,0.00008107506,0.00003119192],"domain_scores_gemma":[0.9987082,0.0004596673,0.0003814539,0.00007190544,0.0002621857,0.0001166228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001744737,0.000121798,0.9556835,0.00006991276,0.00005122434,0.0001076629,0.005799294,0.0002181002,0.002009404,0.0008586079,0.0008308816,0.03407503],"study_design_scores_gemma":[0.000007258869,0.0001685019,0.9737767,0.0000399679,0.00001705412,0.0001779107,0.02119077,0.001761354,0.0004408609,0.000758728,0.001642655,0.0000182143],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967973,0.00003863286,0.0005151709,0.00005101289,0.000007106623,0.00001499858,0.00006290559,0.000003462048,0.002509376],"genre_scores_gemma":[0.9985233,0.00004185659,0.001055774,0.00001583169,0.000004002572,0.00002409798,0.00008831134,0.000001694085,0.0002452582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001983501,"threshold_uncertainty_score":0.006635427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1140873199825143,"score_gpt":0.4081729416221638,"score_spread":0.2940856216396495,"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."}}