Skill UI

A comprehensive guide detailing how to convert various citation styles (such as APA or Harvard author-date) into the specific, numbered format required by the Proceedings of the National Academy of Sciences (PNAS). It covers numbered citations by order of appearance, full author listing, abbreviated journal titles, and specific formats for articles, books, and datasets, ensuring manuscripts meet high academic publishing standards.

This comprehensive framework guides the rigorous design and defense of research in public administration. It covers essential causal inference methodologies, including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), and various experimental designs on bureaucrats and citizens. Use this guide when defining identification, responding to reviewer critiques regarding causality, or pre-registering a study, ensuring the design is both academically sound and practically relevant to public policy.

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

This strategy guides researchers in anticipating the specific, rigorous methodological objections raised by top-tier academic referees, especially in applied econometrics. It provides a systematic map connecting econometric designs (like DID, IV, RD) to common threats (e.g., measurement error, bias, specification issues). Users can pre-empt these criticisms by structuring their manuscript to include necessary defenses, robustness checks, and scope limitations before formal submission.

A comprehensive guide for tackling a 'revise-and-resubmit' academic review (R&R). This process details how to substantively resolve critical, first-order concerns—such as identification threats, construct measurement, or model assumptions—before drafting the letter. It emphasizes rigorous analysis, structured point-by-point responses, maintaining manuscript consistency, and preserving anonymity for resubmission.

This comprehensive guide provides authors with a strategic framework for submitting academic manuscripts to top-tier journals. It details best practices for suggesting and opposing expert reviewers, and, crucially, performing an 'objection pre-mortem.' By anticipating common critiques (e.g., lack of novelty, identification threats) and preparing robust counter-arguments beforehand, authors can significantly strengthen their paper and increase their chances of acceptance.

A comprehensive audit checklist designed for authors submitting papers to the SIGIR conference. It guides users through critical pre-submission checks, covering adherence to specific full/short paper formats, mandatory anonymization techniques (e.g., stripping system names, log provenance), cross-track submission rules, AI disclosure requirements, and common pitfalls that lead to desk rejection.

This guide helps researchers determine the most appropriate conference (UAI, AISTATS, NeurIPS, ICML, etc.) for their work, particularly when the core contribution revolves around uncertainty representation, probabilistic reasoning, or causal inference. It provides detailed filters and comparison points to refine the paper's scope and target audience.

A comprehensive guide for producing the mandatory supplementary video figure for UIST submissions. This material treats the 3-minute video as core evidence, detailing requirements for resolution (1080p/4K), codec (H.264), and anonymity. It guides authors on scripting the video around key claims, ensuring uncut interaction loops, and maintaining academic rigor to convince reviewers of the system's reality.

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.

This guide provides a structured framework for conducting and presenting robust checks in econometric manuscripts, particularly relevant for studies using developing-country data. It moves beyond simple appendix dumping by organizing tests around potential identification threats and specific data-quality risks (e.g., measurement error, coverage gaps). It helps ensure the core findings are load-bearing against sophisticated academic scrutiny.

This comprehensive guide provides authors with structured frameworks for assessing robustness and trustworthiness in World Development manuscripts. It guides users to organize checks by identifying specific threats (e.g., specification dependence, measurement error) in quantitative work, or demonstrating methodological rigor (e.g., triangulation, negative-case analysis) in qualitative work. It ensures findings are thoroughly vetted against alternative explanations.