{"id":"W1549254143","doi":"10.19173/irrodl.v16i1.1948","title":"Big(ger) data as better data in open distance learning","year":2015,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Context (archaeology); Distance education; Inclusion (mineral); Data science; Computer science; Open data; Descriptive statistics; Higher education; Political science; Sociology; World Wide Web; Pedagogy; Social science; Mathematics; Statistics; Geography; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03613171,0.0007570098,0.00102037,0.003768669,0.006180219,0.04991685,0.004938081,0.007297656,0.01340539],"category_scores_gemma":[0.08706199,0.0007252421,0.001065626,0.00818604,0.02304923,0.07326386,0.02290293,0.01077095,0.003867478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006034191,"about_ca_system_score_gemma":0.01123256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090235,"about_ca_topic_score_gemma":0.006323194,"domain_scores_codex":[0.9601288,0.02488655,0.002189477,0.002383953,0.008753918,0.001657311],"domain_scores_gemma":[0.8774561,0.07797455,0.005310315,0.02094443,0.01114777,0.007166957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008456596,0.0001204448,0.005214914,0.001179617,0.00005755628,0.000411489,0.01696051,0.001589419,0.0004768347,0.788322,0.06600393,0.1195787],"study_design_scores_gemma":[0.00001477923,0.00004972537,0.001774132,0.001675416,0.00001834687,0.0002407137,0.02219008,0.001784751,0.0006455554,0.5382618,0.4332676,0.00007717621],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02705978,0.01447759,0.1985124,0.5780646,0.009251656,0.0007184321,0.002132151,0.0008733381,0.16891],"genre_scores_gemma":[0.586376,0.02020675,0.2521119,0.08356709,0.008259225,0.001282289,0.002146985,0.001104445,0.04494528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04991685,"threshold_uncertainty_score":0.191085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4812112097309015,"score_gpt":0.5063618947980583,"score_spread":0.02515068506715679,"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."}}