{"id":"W4416958876","doi":"10.64628/aam.5gsm9dvqh","title":"AI promises efficiency, but it’s also amplifying labour inequality","year":2025,"lang":"","type":"article","venue":"","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inequality; Work (physics); Government (linguistics); Wage inequality; Context (archaeology)","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":[0.005231563,0.0005569973,0.000626335,0.002121223,0.003429281,0.01042565,0.001161365,0.002089991,0.02143502],"category_scores_gemma":[0.009453813,0.0002172551,0.0007661529,0.002671531,0.01158033,0.009692673,0.007102045,0.003280232,0.004288888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002882517,"about_ca_system_score_gemma":0.003261141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002632177,"about_ca_topic_score_gemma":0.002694005,"domain_scores_codex":[0.995575,0.001760135,0.0001676942,0.000446132,0.001241182,0.0008099344],"domain_scores_gemma":[0.990096,0.005151581,0.0007564239,0.001833003,0.001168388,0.0009946153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008860024,0.0001584666,0.004009681,0.0003976539,0.00006269828,0.00007384986,0.003303574,0.0006604734,0.001572596,0.8794413,0.0123091,0.09792202],"study_design_scores_gemma":[0.00004371715,0.0001269273,0.006193932,0.0004233629,0.00003843989,0.0001370685,0.007007338,0.00111802,0.00168727,0.7534713,0.2297144,0.0000383058],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.04955967,0.003753452,0.03185976,0.08924998,0.001094239,0.00009755806,0.0002532093,0.0002726056,0.8238595],"genre_scores_gemma":[0.9170985,0.003026306,0.01239232,0.007757764,0.001082441,0.0001690483,0.0001761661,0.0001826421,0.05811472],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.02143502,"threshold_uncertainty_score":0.07170731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02853849040888901,"score_gpt":0.3204408622413503,"score_spread":0.2919023718324613,"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."}}