{"id":"W2977724409","doi":"10.1007/s10833-019-09353-3","title":"Using theories of action approach to measure impact in an intelligent way: A case study from Ontario Canada","year":2019,"lang":"en","type":"article","venue":"Journal of Educational Change","topic":"Educational Assessment and Improvement","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Dialogic; Meaning (existential); Action (physics); Process (computing); Scale (ratio); Value (mathematics); Measure (data warehouse); Computer science; Action research; Knowledge management; Sociology; Pedagogy; Psychology; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001569904,0.000114308,0.0002749456,0.0003829128,0.00004873725,0.00008349709,0.0003188659,0.00002771586,0.001052701],"category_scores_gemma":[0.0002455896,0.00008000068,0.0000695494,0.0004422603,0.00001181869,0.0006598013,0.00003678308,0.0001699789,0.000003254892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522653,"about_ca_system_score_gemma":0.004143511,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8813089,"about_ca_topic_score_gemma":0.934763,"domain_scores_codex":[0.9974368,0.0001728487,0.000720803,0.0001952384,0.001342404,0.000131887],"domain_scores_gemma":[0.9976227,0.0004906436,0.0005692036,0.0002262518,0.000929304,0.0001618716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009440039,0.001466852,0.969083,0.000002780065,0.00004757899,0.00000512838,0.02607406,0.0005569871,0.0003947178,0.0002468972,0.0004117642,0.001615888],"study_design_scores_gemma":[0.000284019,0.0004332924,0.8776911,0.00002985779,0.00002235181,0.0001023966,0.1171535,0.0001719298,0.0001008455,0.003706074,0.0001919479,0.000112683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972504,0.00006439401,0.00006682533,0.0006708791,0.001331978,0.0004071814,0.00001709563,4.993671e-7,0.0001907777],"genre_scores_gemma":[0.9980705,6.173336e-7,0.001128175,0.0001225099,0.0004678571,0.00001169167,0.00000624902,0.000006169872,0.000186247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09139184,"threshold_uncertainty_score":0.9998605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4563545725511342,"score_gpt":0.4913420883245647,"score_spread":0.03498751577343057,"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."}}