{"id":"W4387344871","doi":"10.1145/3610207","title":"Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"Microsoft Research","keywords":"Software deployment; Stakeholder; Emerging technologies; Digitization; Ambiguity; Knowledge management; Public relations; Situated; Sociotechnical system; Sociology; Computer science; Political science","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":[0.01731958,0.0009938499,0.0003578738,0.001470554,0.005707336,0.009111303,0.001639548,0.002884833,0.002186081],"category_scores_gemma":[0.01573447,0.0005642871,0.000645612,0.0008057637,0.01123539,0.01283964,0.01163171,0.002706929,0.0004404754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003088085,"about_ca_system_score_gemma":0.002708637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005034073,"about_ca_topic_score_gemma":0.005777277,"domain_scores_codex":[0.9888381,0.008520638,0.0002283959,0.0005531619,0.0009880277,0.0008715656],"domain_scores_gemma":[0.9904019,0.006145019,0.0006244733,0.0007801525,0.001134491,0.0009139532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001700989,0.0002732919,0.04547469,0.001040224,0.00006318593,0.001954547,0.6954644,0.005999641,0.01269674,0.1413638,0.004956249,0.09054306],"study_design_scores_gemma":[0.00002063673,0.0002134319,0.008656079,0.001163511,0.00006802724,0.000483089,0.7484055,0.008556534,0.005902618,0.1253768,0.1010412,0.0001125574],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6145323,0.003524979,0.200575,0.07971869,0.0004042618,0.0005538,0.000274864,0.0003549051,0.1000612],"genre_scores_gemma":[0.9824198,0.0008539411,0.0139164,0.0008618038,0.00003902235,0.0001882683,0.00004521145,0.0000296747,0.001645946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01731958,"threshold_uncertainty_score":0.09159577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05822691235665016,"score_gpt":0.293772252811522,"score_spread":0.2355453404548718,"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."}}