{"id":"W2750152232","doi":"10.1177/0162243917727353","title":"Target Practice","year":2017,"lang":"en","type":"article","venue":"Science Technology & Human Values","topic":"Anthropology: Ethics, History, Culture","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research","keywords":"Strategist; Sociotechnical system; Leverage (statistics); Duty; Politics; Sociology; Epistemology; Political science; Political economy; Operations research; Engineering; Computer science; Law; Management; Economics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["sts"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["sts"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.004448608,0.0001676009,0.0002352391,0.0005111583,0.0294942,0.0003348045,0.004096948,0.0005491807,0.0005088527],"category_scores_gemma":[0.01270351,0.0001612006,0.00006037381,0.0006338941,0.1104069,0.001860177,0.0005209976,0.000802068,0.0003995251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003876631,"about_ca_system_score_gemma":0.0008232124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001594681,"about_ca_topic_score_gemma":0.001065638,"domain_scores_codex":[0.9972493,0.000148563,0.0002228954,0.0007191356,0.000827737,0.0008323835],"domain_scores_gemma":[0.9972978,0.00009002284,0.0004627223,0.001341948,0.0006597351,0.0001477195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002950241,0.00006882361,0.005892681,0.000002939488,0.000009703855,0.00002706241,0.01714309,8.522446e-7,0.004762361,0.9640889,0.0066799,0.001320687],"study_design_scores_gemma":[0.0001997219,0.0001259999,0.002615521,0.00002487037,0.0000293479,0.0000132973,0.08277965,0.000006243235,0.003059008,0.2876758,0.623104,0.0003665239],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1167419,0.0008856321,0.0002194103,0.1355889,0.002778392,0.0004411721,0.00000457472,0.001416742,0.7419233],"genre_scores_gemma":[0.981902,0.00007573159,0.007193906,0.0006426547,0.0002701597,0.00001968098,6.447959e-7,0.00001329821,0.009881909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8651601,"threshold_uncertainty_score":0.9956129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05912782586019436,"score_gpt":0.4472395336270975,"score_spread":0.3881117077669031,"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."}}