{"id":"W2985469877","doi":"10.22215/etd/2018-13230","title":"Modeling Meaning a Kantian Intervention in Vector Space Semantics","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Set (abstract data type); Meaning (existential); Space (punctuation); Proof-theoretic semantics; Semantics (computer science); Theoretical computer science; Architecture; Logical conjunction; Artificial intelligence; Cognitive science; Cognitive architecture; Logical framework; Operational semantics; Epistemology; Programming language; Cognition; Computational semantics; Philosophy; Psychology","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":[0.001812173,0.0004382661,0.0003272459,0.0006894841,0.001207196,0.003413181,0.0009884321,0.000885967,0.004832014],"category_scores_gemma":[0.004855162,0.0003481571,0.0009336616,0.0007210874,0.00614524,0.008107864,0.002510932,0.001848216,0.0006022987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433429,"about_ca_system_score_gemma":0.001429124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002307874,"about_ca_topic_score_gemma":0.002152138,"domain_scores_codex":[0.9987299,0.0007602026,0.00006686017,0.0001600903,0.0002004691,0.0000824564],"domain_scores_gemma":[0.9988913,0.0005073697,0.00007545248,0.0003165802,0.0001524747,0.00005680513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001694773,0.000008568967,0.0001911051,0.00002552127,0.000007841916,0.00002307872,0.0006883304,0.005378536,0.0004561446,0.9873641,0.0002596893,0.005580086],"study_design_scores_gemma":[0.000014908,0.00002338862,0.0001198911,0.00001983481,0.00001248846,0.00003676989,0.0002694743,0.03412035,0.001140827,0.9565178,0.007710547,0.00001383052],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08137098,0.0002921014,0.8622341,0.002970861,0.0001352995,0.00006735312,0.0001347293,0.0005058605,0.05228869],"genre_scores_gemma":[0.7295786,0.000225091,0.2621981,0.00021831,0.0000421613,0.0001267738,0.0001005504,0.0001564737,0.00735389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004832014,"threshold_uncertainty_score":0.01616466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024234459794354,"score_gpt":0.3306267800141873,"score_spread":0.3103844354162437,"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."}}