{"id":"W3204113814","doi":"10.29173/iasl8306","title":"Weaving a Storytelling Tapestry Using Computational Thinking","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Museum and Library Services","keywords":"Computational thinking; Storytelling; Context (archaeology); Computer science; Weaving; Sequence (biology); Parallel thinking; Critical thinking; Mathematics education; Critical systems thinking; Artificial intelligence; Narrative; Mathematics; Engineering; Art","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":[0.003034358,0.001342666,0.0005005617,0.00285864,0.004613418,0.01059228,0.002272282,0.001714632,0.01545632],"category_scores_gemma":[0.01035112,0.0006256317,0.0008621759,0.001649913,0.007814525,0.01188529,0.009869035,0.003571865,0.002659813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001432475,"about_ca_system_score_gemma":0.0009674121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001066139,"about_ca_topic_score_gemma":0.001999438,"domain_scores_codex":[0.9970462,0.001983006,0.0001009479,0.0003059956,0.0003939567,0.0001698982],"domain_scores_gemma":[0.9917958,0.005133913,0.0003347879,0.001315692,0.0005370568,0.0008827117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003329106,0.0002203097,0.002513707,0.0008810488,0.00008797007,0.002159317,0.218696,0.007032501,0.008139852,0.5607216,0.03902474,0.16019],"study_design_scores_gemma":[0.00009633986,0.0002520291,0.001279309,0.0008739989,0.00005674574,0.001438666,0.04708519,0.01615785,0.004538988,0.3372574,0.590822,0.000141374],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1105801,0.003057983,0.5514331,0.01565396,0.001794011,0.0007357195,0.0007062685,0.002901885,0.313137],"genre_scores_gemma":[0.6532688,0.001793545,0.2899837,0.001357284,0.0004322253,0.0006549701,0.000806268,0.0009206553,0.05078254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01545632,"threshold_uncertainty_score":0.05170655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05020557746458108,"score_gpt":0.2955242692936913,"score_spread":0.2453186918291102,"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."}}