{"id":"W251193708","doi":"10.29173/iasl8208","title":"Planning for Action: Turning Meaningful Data into Programs and Promotion","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Action (physics); Promotion (chess); Plan (archaeology); Action plan; Computer science; Data science; Knowledge management; Public relations; Political science; Management; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2164292,0.002485126,0.002468492,0.009486905,0.01095257,0.03231426,0.006548872,0.006948716,0.01296314],"category_scores_gemma":[0.2521796,0.002277671,0.001778012,0.009537892,0.02552943,0.04045413,0.0170254,0.01550489,0.004379735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01579505,"about_ca_system_score_gemma":0.0786372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01783455,"about_ca_topic_score_gemma":0.01575101,"domain_scores_codex":[0.7869325,0.1787835,0.006881488,0.005564642,0.01681936,0.005018598],"domain_scores_gemma":[0.7081918,0.2184903,0.0106399,0.02274135,0.02466327,0.01527334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001589927,0.0007762272,0.004659998,0.001922779,0.0001340488,0.0001911929,0.04190138,0.004447212,0.0004449624,0.3339296,0.1559511,0.4554825],"study_design_scores_gemma":[0.0001098793,0.0002664361,0.002555519,0.005560267,0.00006748963,0.00004522384,0.07341304,0.002607578,0.00102891,0.6309286,0.28317,0.0002471976],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01045601,0.004121854,0.4037195,0.4299298,0.004383667,0.005571377,0.00173959,0.003483991,0.1365941],"genre_scores_gemma":[0.2395217,0.008832462,0.6894847,0.03173279,0.0009580596,0.0116553,0.00211789,0.001281,0.01441618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2164292,"threshold_uncertainty_score":0.9662823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1590281202866112,"score_gpt":0.3832102126505716,"score_spread":0.2241820923639604,"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."}}