{"id":"W4389484194","doi":"10.24908/ss.v21i4.15763","title":"Employee Surveillance Technologies: Prevalence, Classification, and Invasiveness","year":2023,"lang":"en","type":"article","venue":"Surveillance & Society","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; HEC Montréal; University of Waterloo","funders":"Université du Québec à Montréal","keywords":"Documentation; Work (physics); Public relations; Emerging technologies; Coronavirus disease 2019 (COVID-19); Position (finance); Business; Knowledge management; Data science; Political science; Computer science; Medicine; Artificial intelligence; Engineering","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":[0.01346196,0.0003939448,0.0004052252,0.00821747,0.001839691,0.005137283,0.001270219,0.001305326,0.003049501],"category_scores_gemma":[0.05905826,0.0002742154,0.0006036067,0.004609355,0.004510812,0.006807197,0.004876126,0.001736508,0.0004671877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767619,"about_ca_system_score_gemma":0.001423947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451219,"about_ca_topic_score_gemma":0.002328265,"domain_scores_codex":[0.98521,0.004584199,0.002264967,0.001357911,0.005807563,0.0007753685],"domain_scores_gemma":[0.9098992,0.05008595,0.02474943,0.005520527,0.008240154,0.001504797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000167247,0.0001141019,0.6773265,0.001529992,0.0001136075,0.0002785073,0.04280243,0.0002813779,0.001193846,0.01936177,0.002790757,0.2540399],"study_design_scores_gemma":[0.00001235949,0.000365966,0.8037905,0.007198915,0.0002083601,0.003825815,0.1125847,0.002095668,0.002490241,0.02338755,0.04388938,0.000150607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.914063,0.024275,0.01076931,0.008922017,0.0002836436,0.0002098597,0.001172054,0.00008011804,0.04022513],"genre_scores_gemma":[0.9895942,0.005921674,0.002821644,0.0003739603,0.0001510631,0.00007905639,0.0003145656,0.0000125637,0.0007311932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01346196,"threshold_uncertainty_score":0.07119453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789097943435904,"score_gpt":0.27938831103062,"score_spread":0.2514973315962609,"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."}}