{"id":"W2922017461","doi":"","title":"“ALLEGIANCE!”: LITERARY TRANSLATION OF REFERENCE NETWORKS IN LEACOCK’S COMIC SKETCHES","year":2017,"lang":"en","type":"article","venue":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Literal translation; Comics; Object (grammar); Allegiance; Atmosphere (unit); Process (computing); Linguistics; Computer science; History; Sociology; Artificial intelligence; Politics; Philosophy; Law; Political science; Source text","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002747273,0.0005637656,0.0002533761,0.002160189,0.006824492,0.007091723,0.0008274529,0.001174388,0.01210488],"category_scores_gemma":[0.0142173,0.0002453334,0.0001529171,0.001974401,0.01415635,0.003951306,0.003230245,0.002073971,0.00152923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009105425,"about_ca_system_score_gemma":0.003624714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0431881,"about_ca_topic_score_gemma":0.09729152,"domain_scores_codex":[0.9968732,0.002143636,0.00006610365,0.0002063145,0.0005455586,0.000165237],"domain_scores_gemma":[0.9941376,0.003715123,0.0002490978,0.0008419456,0.000844192,0.0002120628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006664875,0.00001585789,0.0005079907,0.0002428077,0.000005632949,0.0008196658,0.5151627,0.0002908435,0.001486996,0.4220552,0.0169071,0.04243853],"study_design_scores_gemma":[0.000009118325,0.00002110438,0.0008405411,0.0005641367,0.000008309553,0.000463082,0.1197963,0.00046104,0.001396045,0.01347182,0.8629424,0.0000261254],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1329171,0.005251278,0.02500422,0.01611344,0.002297342,0.0001249931,0.0002967293,0.0004031291,0.8175918],"genre_scores_gemma":[0.9162371,0.001746744,0.005372636,0.0005428291,0.0002205285,0.00005751172,0.000127346,0.0003885558,0.07530673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0431881,"threshold_uncertainty_score":0.08587343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1854752631070841,"score_gpt":0.4055779114406365,"score_spread":0.2201026483335524,"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."}}