Participatory provenance auditing of Canada's AI strategy consultation shows official AI summaries exclude 15-17% of participants more than random baselines, with 33-88% exclusion for dissent clusters.
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A paraphrase-robust clustering pipeline plus XGBoost classifier identifies refactoring-worthy step subsequences in large BDD test corpora with out-of-fold F1 0.891, outperforming rule baselines and LLM judges.
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Participatory provenance as representational auditing for AI-mediated public consultation
Participatory provenance auditing of Canada's AI strategy consultation shows official AI summaries exclude 15-17% of participants more than random baselines, with 33-88% exclusion for dissent clusters.
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Mining Subscenario Refactoring Opportunities in Behaviour-Driven Software Test Suites: ML Classifiers and LLM-Judge Baselines
A paraphrase-robust clustering pipeline plus XGBoost classifier identifies refactoring-worthy step subsequences in large BDD test corpora with out-of-fold F1 0.891, outperforming rule baselines and LLM judges.