{"id":"W2049217144","doi":"10.1145/506740.506745","title":"Using an adapted grounded theory approach for inductive theory building about virtual team development","year":2000,"lang":"en","type":"article","venue":"ACM SIGMIS Database the DATABASE for Advances in Information Systems","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Grounded theory; Axial coding; Coding (social sciences); Computer science; Perspective (graphical); Coding theory; Process (computing); Human–computer interaction; Qualitative research; Theoretical computer science; Artificial intelligence; Sociology; Theoretical sampling; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04067511,0.00125991,0.001403984,0.007562078,0.003939277,0.007546916,0.004032307,0.002015246,0.006927249],"category_scores_gemma":[0.05496451,0.0009163067,0.001960949,0.009165387,0.007921369,0.006436151,0.008287095,0.005789313,0.001441119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006542305,"about_ca_system_score_gemma":0.009941209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003877317,"about_ca_topic_score_gemma":0.007138675,"domain_scores_codex":[0.9567239,0.03381026,0.00170149,0.002337436,0.004681116,0.0007457985],"domain_scores_gemma":[0.9463655,0.04372207,0.001042593,0.004259424,0.004097747,0.0005126014],"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":[0.00008502675,0.0003922635,0.001225092,0.002083988,0.000124407,0.0005384387,0.1195717,0.007531447,0.003573305,0.623224,0.005686472,0.2359639],"study_design_scores_gemma":[0.0003776669,0.0003854649,0.001343099,0.003020103,0.0001091462,0.0005274809,0.04879127,0.02219349,0.006002917,0.771447,0.1455874,0.0002149579],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004927532,0.0001154665,0.9791723,0.0008472117,0.0001365099,0.005518998,0.0004041808,0.0001375565,0.008740202],"genre_scores_gemma":[0.03166753,0.0001726071,0.954802,0.0003769467,0.00002208092,0.0112858,0.0003314723,0.00006164435,0.001279987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04067511,"threshold_uncertainty_score":0.2151131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05489389213379445,"score_gpt":0.3498738756906924,"score_spread":0.294979983556898,"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."}}