{"id":"W2626402840","doi":"","title":"On ironic puns in Portuguese authentic oral data: how does multiple meaning make irony work?","year":2017,"lang":"en","type":"book","venue":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Linguistic Association","funders":"","keywords":"Irony; Meaning (existential); Portuguese; Pun; Linguistics; Philosophy; Epistemology","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":[0.00211032,0.0005509455,0.0003678395,0.001333689,0.002784528,0.005635436,0.0007557739,0.001309817,0.007016964],"category_scores_gemma":[0.01434657,0.0003813274,0.0002815242,0.00121642,0.007052484,0.008002298,0.002300779,0.002883024,0.0008929023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001895644,"about_ca_system_score_gemma":0.001385581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002519901,"about_ca_topic_score_gemma":0.006077266,"domain_scores_codex":[0.9981679,0.0009514347,0.00008232484,0.0001874576,0.0004712302,0.0001396838],"domain_scores_gemma":[0.9925167,0.005601561,0.0004204634,0.0006125664,0.0007030369,0.0001456968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001441886,0.000073193,0.002851845,0.000640852,0.0000147396,0.0023482,0.4033397,0.0002170176,0.003322159,0.4500209,0.02548909,0.1115381],"study_design_scores_gemma":[0.00003181464,0.0001374957,0.02022962,0.003250478,0.000050479,0.006683811,0.283422,0.001999198,0.004159848,0.1679446,0.5119908,0.00009987172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2379433,0.005975036,0.03429173,0.01357619,0.00160839,0.0001021314,0.0003732818,0.0002318522,0.7058981],"genre_scores_gemma":[0.9422676,0.001424011,0.005805327,0.0005861772,0.0003316775,0.0000420965,0.0002491699,0.0003530357,0.04894082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007016964,"threshold_uncertainty_score":0.02347404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027273975442339,"score_gpt":0.3963822976933754,"score_spread":0.2936549001491415,"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."}}