{"id":"W4403763010","doi":"10.1215/2834703x-12347636","title":"What's with All the Tapestries? Intersectionality and the Discursive Vacuum of Generative AI","year":2024,"lang":"en","type":"preprint","venue":"Critical AI","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Intersectionality; Generative grammar; Sociology; Aesthetics; Art; Gender studies; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001511348,0.0001556478,0.0002821808,0.00002039506,0.000557693,0.001541498,0.0003373613,0.0002756559,0.00006562995],"category_scores_gemma":[0.002042888,0.00007470041,0.0001329935,0.0001047772,0.00791656,0.0002925237,0.0005384447,0.001679274,0.00000432139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006693305,"about_ca_system_score_gemma":0.0006468804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003085338,"about_ca_topic_score_gemma":0.003465863,"domain_scores_codex":[0.9980745,0.0006721316,0.0002074938,0.0002853077,0.0005155487,0.0002450437],"domain_scores_gemma":[0.9969349,0.001954617,0.00005377921,0.0002043631,0.0007333218,0.0001190324],"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":[0.0000428688,0.00002654298,0.00001319541,0.00006946202,0.0001293354,0.000003888031,0.07065697,0.000003072056,0.000002077621,0.9260095,0.002743104,0.0002999877],"study_design_scores_gemma":[0.0001195028,0.00004370648,0.0001086077,0.0002379298,0.0001732629,8.976188e-7,0.04003284,0.0000419187,0.00003288415,0.9538994,0.005195773,0.0001132148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0289795,0.001774906,0.0004169302,0.9577745,0.001830483,0.0004602753,0.0000649526,0.00002796611,0.008670535],"genre_scores_gemma":[0.9869754,0.0007687517,0.00004737108,0.0109296,0.0007529363,0.00004332213,0.0000056529,0.00001136147,0.0004655671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.957996,"threshold_uncertainty_score":0.999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04770714946549857,"score_gpt":0.4210242470513718,"score_spread":0.3733170975858732,"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."}}