{"id":"W3010707504","doi":"10.1515/pjbr-2020-0003","title":"A narrative approach to human-robot interaction prototyping for companion robots","year":2020,"lang":"en","type":"article","venue":"Paladyn Journal of Behavioral Robotics","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council; European Commission","keywords":"Narrative; Human–computer interaction; Robot; Computer science; Human–robot interaction; Common ground; Proof of concept; Rapid prototyping; Artificial intelligence; Psychology; Communication; Engineering; Art","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":[0.005518161,0.0006741558,0.0002622453,0.0006449316,0.001300764,0.002759085,0.00144157,0.0008102757,0.01051789],"category_scores_gemma":[0.009653153,0.0003623722,0.0003977512,0.0002383501,0.002904443,0.002075502,0.002954125,0.0008088119,0.000914839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007191357,"about_ca_system_score_gemma":0.000726453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003775539,"about_ca_topic_score_gemma":0.0006586827,"domain_scores_codex":[0.9956671,0.003588894,0.00007820597,0.000159493,0.0003960684,0.0001102439],"domain_scores_gemma":[0.9917496,0.006465955,0.0002900544,0.0005557921,0.0005733664,0.0003651977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001089299,0.002062953,0.004562093,0.005203375,0.00009702577,0.005674644,0.1977839,0.01302223,0.1245529,0.2906942,0.009136728,0.3461208],"study_design_scores_gemma":[0.0007018076,0.006112305,0.007804687,0.002410211,0.0001563722,0.009023628,0.08263891,0.0757147,0.135429,0.07643802,0.6032074,0.0003629596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1671132,0.0008902393,0.7729637,0.001292799,0.0002197877,0.002462373,0.0001221323,0.000668719,0.0542671],"genre_scores_gemma":[0.5418606,0.0005232258,0.4447224,0.0001880427,0.0000280927,0.001820292,0.00005965777,0.00010495,0.01069275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01051789,"threshold_uncertainty_score":0.03518587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2227004791780564,"score_gpt":0.4536369419067631,"score_spread":0.2309364627287066,"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."}}