Skill UI

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This guide provides a rigorous framework for strengthening economic manuscripts submitted to top journals, such as AEJ: Micro. It details how to systematically test the robustness of main results through theoretical extensions (e.g., relaxing assumptions, perturbations) and applied/experimental methods (e.g., sensitivity analyses, placebo tests). Learn how to identify extensions that truly 'earn their place' and significantly enhance the overall academic contribution of your work.

This guide outlines best practices for authors presenting theoretical and empirical results for top-tier journals like the American Economic Journal: Microeconomics (AEJ: Micro). It covers structuring propositions, building transparent numerical examples, creating schematic figures, and formatting tables (including mandated use of standard errors over significance asterisks) to ensure maximum clarity and adherence to academic publishing standards.

Provides comprehensive guidelines for structuring theoretical economics manuscripts for submission to AEJ: Micro. It enforces the theory-first approach, ensuring the introduction immediately presents the economic question and states the main result in words, thus maximizing impact for a broad micro audience. It also covers specific AEA house style conventions, such as reporting standard errors and structuring the abstract.

A comprehensive guide for building and strengthening the conceptual or theoretical framework for academic manuscripts, particularly for submission to top-tier journals like AERJ. It teaches researchers how to ensure that theory drives the entire study—from question generation and design choices to data analysis and interpretation—transforming findings from mere reports into genuine theoretical contributions.

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

A comprehensive guide for structuring academic arguments, specifically tailored to the high theoretical standards of top-tier journals like AJPS. It helps researchers transform initial ideas into a rigorous framework, defining core mechanisms, stating directional and testable hypotheses before results, and establishing clear scope conditions for empirical, formal, or methodological papers.

This skill provides a rigorous framework for political scientists to pre-test their research projects against the standards of the American Journal of Political Science (AJPS). It evaluates whether a topic has a sharp, generalizable question, demonstrates strong causal identification, provides a substantive theoretical contribution, and fits the appropriate submission type (Article, Research Note, Correspondence). Use this before committing significant time to writing.

This guide provides detailed standards for planning, auditing, and executing data analysis for sociological manuscripts, specifically tailored to the standards of the American Journal of Sociology (AJS). It stresses that analysis must credibly support a theoretical claim, rather than simply showcasing technical proficiency. Key areas covered include quantitative robustness checks, comparative-historical logic, ethnographic procedure, and the crucial principle of triangulation across diverse data sources.

This guide helps authors strategically position their academic work within the existing body of sociological literature. It teaches how to frame a paper not merely as filling a knowledge gap, but as resolving a core theoretical tension or participating in a live, generalist debate. It is essential for improving submissions to highly selective, theory-driven journals like AJS.

A comprehensive guide for responding to reviews and comments for the American Journal of Sociology (AJS). It structures persuasive, evidence-based rebuttal letters for Revise-and-Resubmit (R&R) stages or formal author replies to published Comments. Focuses on maintaining academic rigor, addressing conflicting points, and advancing the field's understanding through measured, theoretical engagement.