{"id":"W4384666239","doi":"10.1080/10301763.2023.2230953","title":"Labour shortages: a game changer for industrial relations?","year":2023,"lang":"en","type":"article","venue":"Labour & Industry a journal of the social and economic relations of work","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Industrial relations; Negotiation; Economic shortage; Collective bargaining; Labour economics; Solidarity; Relevance (law); Bargaining power; Disadvantage; Wage; Power (physics); Labor relations; Successor cardinal; Economics; Political science; Business; Management; Law; Government (linguistics)","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":[0.007616004,0.0005482955,0.0005512186,0.001233246,0.01669506,0.0153665,0.002546179,0.00438097,0.01595458],"category_scores_gemma":[0.01108478,0.0003254662,0.0004766285,0.001462586,0.0223343,0.01341373,0.008998848,0.005700424,0.001640171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01935356,"about_ca_system_score_gemma":0.01693333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1031211,"about_ca_topic_score_gemma":0.1578138,"domain_scores_codex":[0.993861,0.003399699,0.0001069187,0.0004098689,0.0008006153,0.001421943],"domain_scores_gemma":[0.9934197,0.002416117,0.0006657105,0.0002744353,0.0007441674,0.00247991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002342574,0.0004009379,0.01076361,0.000498297,0.00004468179,0.002060886,0.2426741,0.001171186,0.001302058,0.4756961,0.1025432,0.1626107],"study_design_scores_gemma":[0.00005102171,0.0001517238,0.005134103,0.000497744,0.00001360103,0.0003590992,0.3347203,0.001447148,0.0002744711,0.1253078,0.5319774,0.00006560803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1406669,0.00811312,0.01234113,0.576812,0.003984854,0.0001333909,0.0001287079,0.0002083191,0.2576116],"genre_scores_gemma":[0.9309977,0.002873539,0.002706605,0.02501709,0.0008795149,0.0001332992,0.00006095007,0.00009681609,0.03723438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1031211,"threshold_uncertainty_score":0.2050417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05317780234998593,"score_gpt":0.2978814525401376,"score_spread":0.2447036501901517,"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."}}