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Model and Theory Development for POM
pom-theory-development
brycewang-stanford/Awesome-Journal-Skills
299
Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.
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SoCC Paper Writing Style Guide
socc-writing-style
brycewang-stanford/Awesome-Journal-Skills
67
This comprehensive guide provides strict structural and technical guidelines for authors submitting papers to ACM SoCC. It mandates focusing on cloud-scale problems recognized by operators, emphasizing contributions at the intersection of systems and data. Key requirements include reporting tail latency (p95/p99) and concrete cost models, grounding every claim in measured, real-world evidence, and explicitly stating external limitations.
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Theory and Model Craft for Development Economics
wber-theory-model
brycewang-stanford/Awesome-Journal-Skills
246
A comprehensive guide detailing when and how to structure theoretical models in development economics research, particularly for World Bank Economic Review submissions. It emphasizes that models must serve a functional purpose—such as interpreting empirical coefficients, running policy counterfactuals, or aggregating to welfare statements—rather than being decorative. Key principles cover parameter identification, matching structural frictions to the local setting, and handling counterfactual assumptions (Lucas critique).
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Yann LeCun's AI Critique and Debates
yann-lecun-debate
sickn33/agentic-awesome-skills
452
A specialized module containing Yann LeCun's comprehensive arguments on the limitations of Large Language Models (LLMs). It critically examines the concept of 'emergent reasoning,' contrasts LLMs' statistical prediction with genuine world models, and addresses deep technical rivalries (e.g., LeCun vs. Hinton). Ideal for advanced discussions on AI causality, common sense, and AGI feasibility.
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