{"id":"W4416004729","doi":"10.5465/amproc.2025.18809poster","title":"Decoding Ethical Affordances in HR Algorithms Through an Actor-Network Theory Perspective","year":2025,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Affordance; Scholarship; Perspective (graphical); Unintended consequences; Field (mathematics); Action (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["sts"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["sts"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001173839,0.0002642836,0.0003596529,0.0004847274,0.0002446054,0.000167069,0.0008656317,0.0003831875,0.00004716605],"category_scores_gemma":[0.0001428223,0.0002467233,0.00008354877,0.001400386,0.0002581835,0.002292696,0.0007241165,0.0007756896,0.00001732285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095732,"about_ca_system_score_gemma":0.000006811713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005741931,"about_ca_topic_score_gemma":0.000008286353,"domain_scores_codex":[0.998185,0.000007018094,0.0004232946,0.0005642253,0.0003171672,0.0005032387],"domain_scores_gemma":[0.9994751,0.00006306469,0.0002693704,0.00009685323,0.00008687771,0.000008684241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007712391,0.00006363537,0.006907639,0.0003683369,0.00007773402,0.000001935721,0.0002543662,0.00002124398,0.00003809244,0.9710951,0.005868373,0.01522638],"study_design_scores_gemma":[0.0005828119,0.0000184307,0.01727916,0.0004342705,0.0001053152,3.292222e-7,0.01455121,0.0007990925,0.0002791794,0.9238228,0.04182374,0.0003036997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3831945,0.001428895,0.004014086,0.05099317,0.0004287745,0.001606603,0.000001798433,0.001240471,0.5570917],"genre_scores_gemma":[0.9852992,0.0005937719,0.006941654,0.00594348,0.0004137588,0.00008299729,0.00000212856,0.00002543962,0.0006975703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6021047,"threshold_uncertainty_score":0.9999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756098793785319,"score_gpt":0.3127645076060069,"score_spread":0.2852035196681537,"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."}}