{"id":"W4296831767","doi":"10.1016/j.destud.2022.101135","title":"Expansive learning for collaborative design","year":2022,"lang":"en","type":"article","venue":"Design Studies","topic":"Design Education and Practice","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de Recherche du Québec-Société et Culture","keywords":"Expansive; Situated; Process (computing); Knowledge management; Action learning; Action (physics); Reflection (computer programming); Situated cognition; Psychology; Engineering; Computer science; Cooperative learning; Pedagogy; Teaching method; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006546008,0.0001307846,0.0001817874,0.00008126715,0.0005660441,0.00002738218,0.00009623111,0.00001807324,0.00009505611],"category_scores_gemma":[0.000504924,0.0001393891,0.00002767421,0.0003870054,0.00002681579,0.0001285441,0.00003001406,0.0001729949,0.00002746863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358342,"about_ca_system_score_gemma":0.00007305101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.279281e-7,"about_ca_topic_score_gemma":2.817771e-7,"domain_scores_codex":[0.9990309,0.0003345354,0.0001480383,0.0001492597,0.0001363304,0.0002008727],"domain_scores_gemma":[0.99763,0.001975506,0.0000481774,0.00009180579,0.0002209107,0.00003358772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000121474,0.00003320786,0.00001464566,0.00003741547,0.0004074386,0.000005397459,0.02286207,0.6510978,0.00324958,0.0008248601,0.3157757,0.005570476],"study_design_scores_gemma":[0.0009982928,0.0007487538,0.00006338926,0.00001305056,0.0001392385,0.00001561179,0.1412656,0.03255469,0.009821507,0.001991134,0.811802,0.0005866436],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000444181,0.009772806,0.9843935,0.000479644,0.001228572,0.001055419,0.00001095948,0.0004117309,0.002203234],"genre_scores_gemma":[0.894558,0.001363359,0.09044651,0.0006146433,0.0002826726,0.0060249,0.00002066308,0.0001201342,0.006569135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8941138,"threshold_uncertainty_score":0.5684121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394355717047488,"score_gpt":0.3374829222249707,"score_spread":0.1980473505202218,"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."}}