SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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13 Pith papers cite this work, alongside 470 external citations. Polarity classification is still indexing.
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A queueing model of AI task processing identifies a 'variance wedge' where mean task speed falls but system delay rises due to rework and reduced oversight under congestion.
The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
Fluent AI users adopt an active, iterative collaboration mode that produces more visible failures but better recovery and success on hard tasks, whereas novices experience more invisible failures from passive use.
Shorter LLM response latencies reduce perceived output thoughtfulness and usefulness, while task type affects prompting frequency independently of latency.
The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
The paper claims that alignment requires treating AI as part of the self through cognitive co-regulation, identifying risks like deskilling and automation bias while drawing on System 0 cognition theory.
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
Adopting AI does not guarantee productivity boosts due to five moderating factors (human resource composition, baseline capability, learning curve, incentives for fair use, and objective flexibility), by revising an existing economic model.
citing papers explorer
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How to deal with machine learning bias in economic history
The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.
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Human Capital, AI, and Labor Commoditization
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
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From Exposure to Adoption: Generative AI in European Workplaces
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.