{"id":"W2592881435","doi":"10.24242/jclis.v1i1.22","title":"A Case for Critical Data Studies in Library and Information Studies","year":2017,"lang":"en","type":"article","venue":"Journal of Critical Library and Information Studies","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Argument (complex analysis); Critical theory; Sociology; Politics; Epistemology; Big data; Public relations; Work (physics); Engineering ethics; Social science; Political science; Data science; Computer science; Law; Engineering; Medicine","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","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001154139,0.000168281,0.0005864238,0.0004342834,0.0009295569,0.001154995,0.0008207894,0.0001033918,0.000008256235],"category_scores_gemma":[0.03285304,0.0001081833,0.00005251934,0.0002147591,0.001772705,0.1637345,0.002266505,0.0002697333,0.000005559544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005774345,"about_ca_system_score_gemma":0.00004755224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.992358e-7,"about_ca_topic_score_gemma":2.863744e-7,"domain_scores_codex":[0.9976428,0.00006402963,0.001526537,0.0001594081,0.0003821687,0.0002250895],"domain_scores_gemma":[0.9922564,0.006067195,0.000533388,0.0006653701,0.0003659758,0.0001116835],"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":[0.0001705421,0.00005321422,0.001525793,0.0005753097,0.0001480123,0.00006540173,0.003025797,0.000001864601,0.000001889672,0.8612258,0.0627946,0.07041173],"study_design_scores_gemma":[0.001447744,0.0005223265,0.004442104,0.000433435,0.0001336397,0.001718252,0.1742585,0.001329823,0.0001661674,0.3304214,0.4847611,0.0003654369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2321433,0.07163027,0.005828396,0.6766275,0.002766433,0.001412283,0.003238332,0.0002275215,0.006125973],"genre_scores_gemma":[0.9380555,0.04079759,0.0179431,0.002914849,0.000188821,0.00003643832,0.00002232754,0.000006503322,0.00003491108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7059121,"threshold_uncertainty_score":0.9998819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5011702460060182,"score_gpt":0.5034852273212985,"score_spread":0.002314981315280296,"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."}}