{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001911665,0.0007356198,0.0004716203,0.0009061884,0.001269002,0.004230818,0.001289698,0.001517592,0.02127721],"category_scores_gemma":[0.008817033,0.0003711839,0.0006055387,0.0007911911,0.003674486,0.007731048,0.007284032,0.002426264,0.002282226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008346798,"about_ca_system_score_gemma":0.0008548867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004689077,"about_ca_topic_score_gemma":0.0007958679,"domain_scores_codex":[0.9982772,0.0009210093,0.00009792038,0.0002482595,0.0003778454,0.00007779647],"domain_scores_gemma":[0.9910472,0.006698109,0.0001682952,0.00152365,0.0002661509,0.0002965036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002132661,0.00009089651,0.0003002497,0.0002289328,0.00000880582,0.0001026722,0.002459189,0.001621076,0.001033382,0.8771355,0.001826936,0.115171],"study_design_scores_gemma":[0.00001342408,0.00004688298,0.0002731854,0.00022969,0.000009917542,0.0002204581,0.0008222191,0.007601369,0.0008954611,0.9276336,0.06224191,0.00001175661],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02670708,0.004517803,0.7660881,0.002753885,0.0003086637,0.0001940142,0.000065231,0.0009783424,0.1983869],"genre_scores_gemma":[0.4961521,0.002628076,0.4552195,0.0004830835,0.0002535504,0.0005478492,0.000194773,0.0002382574,0.04428281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02127721,"threshold_uncertainty_score":0.07117939,"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."}}