Skill UI

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.

This guide provides a comprehensive framework for understanding the academic peer review process used at AISTATS, covering everything from OpenReview submission workflows and author-reviewer dynamics to advanced decision criteria (correctness, significance, empirical support). It helps researchers strategize their submissions and responses to maximize acceptance chances in statistics and machine learning.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

Provides a detailed guide on the mandatory pre-publication replication and verification process for American Journal of Political Science (AJPS). It instructs authors on building a robust, reproducible package—including code, data, and documentation—that an independent third party can re-run to confirm all numerical findings match the main text before publication.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

This guide outlines the stringent standards for creating tables and figures (exhibits) for the American Journal of Political Science (AJPS). It emphasizes that exhibits must be self-contained, highly accessible, and, critically, regenerated exactly by deposited code for third-party verification. It provides best practices for structuring research results, managing word counts by differentiating between core arguments in the main text and supporting details in the Supporting Information (SI).

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.

A comprehensive guide for structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

A comprehensive guide for preparing academic exhibits (tables, figures, appendices) for the American Journal of Sociology (AJS). This skill ensures strict compliance with journal house conventions, covering consecutive numbering, lettered appendices, mandatory alt text, and proper post-text placement. It helps authors structure their arguments visually and pass through rigorous peer review and production stages.

This framework provides a rigorous methodology for conducting systematic literature reviews and comprehensive academic syntheses. It guides researchers through building transparent coverage, designing robust coding matrices, and choosing the correct integration method (narrative, systematic, or bibliometric) to move beyond simple summarization and create an auditable intellectual map of a field.