Skill UI

This guide details the rigorous review standards and criteria for developmental psychology manuscripts, covering masked peer review, developmental significance, methodological rigor (age/cohort effects, invariance, attrition), JARS reporting, and TOP transparency. Use this resource to stress-test your paper before submission, understand expected revisions, or interpret complex decision letters.

This comprehensive guide helps researchers determine if their empirical project qualifies for a Developmental Psychology journal. It emphasizes that the core contribution must focus on 'developmental change' across the lifespan (age effects, trajectories, or underlying mechanisms), rather than merely describing a single-age phenomenon. It provides a four-gate fit test covering developmental claims, theory advancement, design credibility, and open-science readiness, guiding researchers on framing and proper submission tiering.

A comprehensive guide for structuring the empirical evidence section of top-tier NLP conference papers (e.g., EACL). It mandates rigorous controls, including fair baselines (tuned models, explicit LLM prompts), statistical significance testing (variance, CI), contamination checks, and detailed human evaluation protocols. Use this to elevate your research from mere assertion to scientifically audited proof.

This comprehensive checklist is designed for auditing NLP research papers, particularly for major conferences like EACL. It serves as a claims audit, ensuring that every reported result, claim, or finding—including hyperparameters, compute costs, verbatim prompts, data sources, and significance testing—is fully documented and reproducible. It emphasizes academic rigor and transparency in AI research.

This guide helps researchers determine the optimal academic conference (ECAI, IJCAI, AAAI, etc.) for their AI contribution *before* starting to write. It provides structured tests (like the Model-swap test and General-audience test) and criteria (contribution shape, main track vs. application) to ensure the work is optimally matched to a specialized venue, maximizing the chances of acceptance and impact.

A comprehensive guide for designing and auditing high-stakes computer vision experiments for top-tier conferences. It covers critical areas like 'matched-substrate fairness,' 'six-month-staleness testing,' and rigorous ablation studies, ensuring that claims are robust, reproducible, and withstand intense peer review scrutiny.

A comprehensive guide for empirical economists on building a robust argument beyond simple statistical significance. It details how to address alternative economic explanations, test for specification fragility, and establish external validity, helping manuscripts survive demanding academic referee scrutiny.

This guide assists researchers in structuring and rigorously developing the economic model or mechanism for academic papers. It provides best practices for moving beyond mere empirical findings, ensuring that every result is tied to a clear, testable economic theory, whether through minimal-model discipline or full general equilibrium analysis. Essential for submissions to top-tier economics journals.

A comprehensive guide for structuring and communicating robustness checks in economic policy papers. It teaches authors how to organize tests not as a checklist of alternative specifications, but by the specific threat (e.g., sample size, functional form, pre-trends) they neutralize. The focus must remain on defending the policy-relevant magnitude and confidence interval in the main text, preventing the paper from becoming a mere appendix dump.

This guide outlines rigorous standards for incorporating theoretical and quantitative models into economic policy manuscripts. It emphasizes that models must be minimal, transparent, and directly bear policy weight—converting estimates into statements about welfare, fiscal costs, or clear mechanisms. It teaches how to design models that pass 'the necessity test,' ensuring policymakers can trace headline numbers directly back to key, identifiable assumptions.

A comprehensive pre-submission strategy guide designed to 'red-team' theoretical and econometric manuscripts. This tool helps authors anticipate and pre-empt harsh critiques from expert co-editors and referees, focusing specifically on identifying proof gaps, testing the generality of claims, ensuring finite-sample evidence, and addressing potential methodological competitors before journal submission.

Provides a comprehensive guide on generating and reporting rigorous Monte Carlo and finite-sample evidence for econometrics and statistical methods papers. It details best practices for designing simulations, stress-testing assumption boundaries (degenerate cases), comparing against existing estimators, and ensuring full reproducibility to validate theoretical results for realistic sample sizes.