{"id":"W2745678670","doi":"10.19173/irrodl.v18i5.2551","title":"Revisiting Sensemaking: The case of the Digital Decision Network Application (DigitalDNA)","year":2017,"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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Rigour; Dashboard; Data science; Sensemaking; Stakeholder; Flexibility (engineering); Analytics; Big data; Visualization; World Wide Web; Knowledge management; 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":[0.02291101,0.0008054022,0.0005801705,0.002788818,0.01479208,0.02344449,0.003768449,0.005511331,0.00574669],"category_scores_gemma":[0.03042112,0.0007012058,0.001166777,0.004275853,0.01587693,0.01686279,0.01635602,0.005754629,0.001193033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006895572,"about_ca_system_score_gemma":0.006017666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01387741,"about_ca_topic_score_gemma":0.02219534,"domain_scores_codex":[0.9720002,0.02007492,0.0008392112,0.00196055,0.003726169,0.001398792],"domain_scores_gemma":[0.9746946,0.01774143,0.0009462639,0.002924948,0.002020221,0.001672534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002178482,0.0004269696,0.008439507,0.0007549431,0.00006108942,0.02193118,0.5086453,0.005838196,0.004435395,0.31046,0.01716893,0.1216206],"study_design_scores_gemma":[0.00005087976,0.0001668638,0.002588377,0.0007819686,0.00003837768,0.003178284,0.3578855,0.01743189,0.003015821,0.0983389,0.5163672,0.0001558748],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4240192,0.002094663,0.2196428,0.05902518,0.001243667,0.0009585712,0.0004978392,0.001006328,0.2915118],"genre_scores_gemma":[0.8530323,0.001033018,0.1118465,0.002723917,0.0001486861,0.0003693013,0.0002811718,0.0003808605,0.0301842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02344449,"threshold_uncertainty_score":0.1211665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1302990109552373,"score_gpt":0.4369610695293453,"score_spread":0.306662058574108,"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."}}