{"id":"W2786526525","doi":"10.5210/fm.v23i2.8073","title":"Goals for algorithmic genies","year":2018,"lang":"en","type":"article","venue":"First Monday","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Raising (metalworking); Computer science; Value (mathematics); Fundamental human needs; Data science; Risk analysis (engineering); Business; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007811098,0.00007121727,0.0001103491,0.00003220491,0.001535617,0.0001653122,0.0003249131,0.0001550153,0.0003362907],"category_scores_gemma":[0.001541959,0.00006852869,0.00007797749,0.0001272779,0.0005224693,0.0002285244,0.00004373899,0.00008361818,0.0001431561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005605545,"about_ca_system_score_gemma":0.0001541097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639067,"about_ca_topic_score_gemma":0.001311328,"domain_scores_codex":[0.9991322,0.00004182978,0.0001123342,0.0001434644,0.0002271556,0.00034305],"domain_scores_gemma":[0.9990649,0.0002534188,0.00004984049,0.0001657587,0.0003428021,0.0001233325],"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":[0.00002111234,0.00005887721,0.0007592591,0.00002088853,0.00004757358,0.000001833148,0.6480561,8.884126e-7,0.0001547468,0.06309357,0.2788411,0.00894405],"study_design_scores_gemma":[0.0001336632,0.00008586046,0.000629203,0.000009385823,0.00000751146,8.183602e-8,0.001226032,0.00002748467,0.0002729348,0.03615396,0.96135,0.0001038558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5597644,0.0006810457,0.003274307,0.1888268,0.005283516,0.001660354,0.0001934612,0.0005688222,0.2397473],"genre_scores_gemma":[0.9828135,0.0001203067,0.002535108,0.0009714023,0.002480769,0.00002611248,0.000003747813,0.00001382856,0.01103521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6825089,"threshold_uncertainty_score":0.9997643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05731704506763179,"score_gpt":0.4086526502802241,"score_spread":0.3513356052125923,"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."}}