{"id":"W3201943163","doi":"10.3138/9781781793220-014","title":"12 Contrastive analyses of evaluation in text: Key issues in the design of an annotation system for attitude applicable to consumer reviews in English and Spanish","year":2013,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Annotation; Computer science; Key (lock); Linguistics; Natural language processing; Contrastive analysis; Artificial intelligence; Information retrieval; Philosophy","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":[0.01056702,0.0007684833,0.0005499235,0.00254716,0.0009910846,0.004260611,0.001001567,0.000928956,0.007074439],"category_scores_gemma":[0.02918406,0.0004846938,0.0005200754,0.002056827,0.002195911,0.004588623,0.001408853,0.002500633,0.004002239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002861398,"about_ca_system_score_gemma":0.001537462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400969,"about_ca_topic_score_gemma":0.003995315,"domain_scores_codex":[0.9931838,0.003960608,0.0004898141,0.0006831271,0.001531066,0.0001515541],"domain_scores_gemma":[0.9672254,0.02037074,0.0007763491,0.001072431,0.01033418,0.000220873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002501548,0.0001370923,0.00343488,0.001844595,0.00007409818,0.0005725911,0.03148529,0.0009573699,0.04257658,0.1044085,0.09840213,0.7158567],"study_design_scores_gemma":[0.00007907975,0.0002157104,0.02843479,0.001731722,0.000160159,0.001243234,0.01542152,0.03154873,0.06354118,0.1421701,0.7151747,0.0002790225],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02522316,0.004543051,0.8291992,0.009868412,0.001189868,0.001105105,0.001306604,0.003221545,0.1243431],"genre_scores_gemma":[0.2199256,0.002524806,0.7052212,0.00187792,0.0009609963,0.001758236,0.001959741,0.00278436,0.06298716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01056702,"threshold_uncertainty_score":0.05588442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08577557456558975,"score_gpt":0.3063680702219334,"score_spread":0.2205924956563436,"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."}}