{"id":"W3133773450","doi":"10.1111/ntwe.12187","title":"What do unions do… with digital technologies? An affordance approach","year":2021,"lang":"en","type":"article","venue":"New Technology Work and Employment","topic":"Labor Movements and Unions","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Affordance; Visibility; Core (optical fiber); Trade union; Contrast (vision); Political science; Business; Computer science; Human–computer interaction; International trade; Telecommunications; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003995806,0.0002685958,0.0003328212,0.002310087,0.003842683,0.009141756,0.0008519044,0.001298509,0.006788312],"category_scores_gemma":[0.01116101,0.0003404084,0.0004400818,0.001800515,0.01066873,0.009309014,0.00504215,0.0007935861,0.0003037045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004149289,"about_ca_system_score_gemma":0.002058515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02366947,"about_ca_topic_score_gemma":0.03126129,"domain_scores_codex":[0.9962303,0.001831208,0.0001435061,0.0003505889,0.0008588282,0.0005854918],"domain_scores_gemma":[0.9952123,0.002720325,0.001000088,0.0003350512,0.0003339234,0.0003981549],"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":[0.0002977605,0.0002837387,0.1506396,0.0005974424,0.00008090973,0.0003334126,0.1756933,0.004186428,0.002531513,0.5035787,0.001769531,0.1600076],"study_design_scores_gemma":[0.00005373679,0.0002812704,0.1295017,0.0008921712,0.00009641769,0.0003615058,0.3893243,0.008012493,0.001389328,0.3867846,0.0831677,0.0001347649],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7684906,0.001729571,0.02297185,0.008700488,0.00006781502,0.00005768773,0.00009255683,0.00006237608,0.1978271],"genre_scores_gemma":[0.9978052,0.0001901008,0.001083845,0.00006873114,0.000008801991,0.000009314692,0.000008168064,0.000005823373,0.0008200768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02366947,"threshold_uncertainty_score":0.04706341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618636670946227,"score_gpt":0.2688156926606544,"score_spread":0.2526293259511921,"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."}}