{"id":"W6966327058","doi":"10.3886/e117330","title":"Closing the Word Gap with Big Word Club: Evaluating the Impact of a Tech-Based Early Childhood Vocabulary Program","year":2020,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Vocabulary; Closing (real estate); Test (biology); Vocabulary development; Word (group theory); Entertainment; Control (management); Club","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":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.005595592,0.001900419,0.001773872,0.0006430519,0.0009726618,0.001615769,0.01158236,0.0006433213,0.0001408589],"category_scores_gemma":[0.003037947,0.001028029,0.000606751,0.004799102,0.001633055,0.0008984958,0.003645511,0.004065084,0.0009112591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004411088,"about_ca_system_score_gemma":0.002942088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005403467,"about_ca_topic_score_gemma":0.0003518114,"domain_scores_codex":[0.9891329,0.001211285,0.001658264,0.002678495,0.003502705,0.001816364],"domain_scores_gemma":[0.9825435,0.001207725,0.003283426,0.0120785,0.0004551573,0.0004316614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009851491,0.0007993104,0.000361768,0.0002366515,0.001469804,0.00009002179,0.0004503066,0.0002067186,0.0004747506,0.000001781638,0.9545108,0.04041295],"study_design_scores_gemma":[0.00855524,0.00982583,0.02891101,0.009381613,0.008876818,0.0004490534,0.0007500549,0.007203476,0.0004233839,0.0001714929,0.920455,0.004997034],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0462507,0.0009593997,0.00002090876,0.0004420418,0.0002185575,0.006084466,0.9453859,0.0006155029,0.00002254959],"genre_scores_gemma":[0.03505166,0.00006517232,0.002608895,0.0003636398,0.001087671,0.0005920826,0.9595956,0.0006296993,0.000005608456],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03541592,"threshold_uncertainty_score":0.9998667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093528343442971,"score_gpt":0.3781345134387192,"score_spread":0.2687816790944222,"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."}}