{"id":"W4280528238","doi":"10.1108/jwam-02-2022-0010","title":"Refining virtual cross-national research collaboration: drivers, affordances and constraints","year":2022,"lang":"en","type":"article","venue":"Journal of Work-Applied Management","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Affordance; Computer-supported cooperative work; Originality; Asynchronous communication; Journaling file system; Knowledge management; Negotiation; Computer science; Virtual team; Psychology; Human–computer interaction; Work (physics); Sociology; Engineering; Social psychology; Creativity","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04232634,0.000689035,0.0007654889,0.003229744,0.01042618,0.02213458,0.002373831,0.001728024,0.005766941],"category_scores_gemma":[0.09295344,0.0007591321,0.0008329148,0.0027156,0.01243419,0.01384107,0.02354993,0.002127894,0.0005493401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004399322,"about_ca_system_score_gemma":0.007383016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002729705,"about_ca_topic_score_gemma":0.003335188,"domain_scores_codex":[0.9277126,0.05791125,0.002726757,0.00360028,0.004846199,0.003202883],"domain_scores_gemma":[0.8972329,0.06560108,0.01272996,0.009155366,0.006242679,0.009038012],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002823265,0.0001570925,0.05385049,0.001456561,0.00009965524,0.001592704,0.8038529,0.001140192,0.002818263,0.04366367,0.001490189,0.08959597],"study_design_scores_gemma":[0.00003424193,0.0002755982,0.01814164,0.0008321487,0.00005642498,0.001258463,0.9080416,0.001995213,0.0007888196,0.02691482,0.04156115,0.00009990161],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.906871,0.00152231,0.04282583,0.004552329,0.0002082057,0.000421272,0.0000936322,0.0002012689,0.04330423],"genre_scores_gemma":[0.9906896,0.0002885578,0.007495928,0.0001689625,0.00002332236,0.0002347267,0.00003605306,0.00003412329,0.001028643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9576737,"threshold_uncertainty_score":0.2238458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04135085053468576,"score_gpt":0.3922659360299853,"score_spread":0.3509150854952995,"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."}}