{"id":"W1964078980","doi":"10.2139/ssrn.1026961","title":"Automatic Annotation of Semantic Fields for Political Science Research","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Annotation; Politics; Computer science; Information retrieval; Natural language processing; Data science; Artificial intelligence; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03942231,0.00004490708,0.0001094773,0.0004172608,0.0009000383,0.00005655208,0.0003044723,0.00004549268,0.00002640632],"category_scores_gemma":[0.002377222,0.00003990809,0.00008488609,0.001076519,0.0005146639,0.0001769074,0.00002136769,0.0005382726,0.000003585453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008421621,"about_ca_system_score_gemma":0.005625337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006377256,"about_ca_topic_score_gemma":0.003777527,"domain_scores_codex":[0.9961504,0.0002118102,0.0002692985,0.0001208152,0.0008938377,0.002353803],"domain_scores_gemma":[0.9976881,0.001172351,0.00008725344,0.00006548064,0.0008422444,0.0001446237],"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":[0.000008831078,0.00002905132,0.000630799,0.000004433932,0.00001554875,3.280392e-7,0.0009115983,0.00001096695,0.000199298,0.9213924,0.00001044281,0.07678632],"study_design_scores_gemma":[0.0001550706,0.0001471176,0.004881691,0.00001125461,0.00001560665,0.000009657329,0.01015258,0.002690444,0.0002794329,0.9813663,0.0002392244,0.00005160748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6700452,0.0001624484,0.3228738,0.002677251,0.0001274669,0.0001321872,3.615632e-7,0.00001276219,0.003968532],"genre_scores_gemma":[0.995907,0.00004067737,0.003224154,0.00002758174,0.0002984738,0.000001679639,4.786596e-7,0.000003714521,0.0004962506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3258618,"threshold_uncertainty_score":0.9979107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0662732868529866,"score_gpt":0.4898803160267777,"score_spread":0.4236070291737911,"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."}}